import{s as ws,o as Ms,n as R}from"../chunks/scheduler.25b97de1.js";import{S as Ns,i as $s,g as i,s as n,r as u,A as zs,h as c,f as r,c as s,j as N,u as f,x as m,k as M,y as t,a as l,v as _,d as g,t as T,w as b}from"../chunks/index.d9030fc9.js";import{T as At}from"../chunks/Tip.baa67368.js";import{D as $}from"../chunks/Docstring.e257edda.js";import{C as Dt}from"../chunks/CodeBlock.e6cd0d95.js";import{E as Et}from"../chunks/ExampleCodeBlock.20db4b6e.js";import{H as S,E as Ps}from"../chunks/EditOnGithub.91d95064.js";function xs(w){let o,y="sequence pair mask has the following format:",p,h,k;return h=new Dt({props:{code:"MCUyMDAlMjAwJTIwMCUyMDAlMjAwJTIwMCUyMDAlMjAwJTIwMCUyMDAlMjAxJTIwMSUyMDElMjAxJTIwMSUyMDElMjAxJTIwMSUyMDElMEElN0MlMjBmaXJzdCUyMHNlcXVlbmNlJTIwJTIwJTIwJTIwJTdDJTIwc2Vjb25kJTIwc2VxdWVuY2UlMjAlN0M=",highlighted:`0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 1 </span>1<span class="hljs-number"> 1 </span>1<span class="hljs-number"> 1 </span>1<span class="hljs-number"> 1 </span>1 1
| first sequence    | second sequence |`,wrap:!1}}),{c(){o=i("p"),o.textContent=y,p=n(),u(h.$$.fragment)},l(a){o=c(a,"P",{"data-svelte-h":!0}),m(o)!=="svelte-16klr56"&&(o.textContent=y),p=s(a),f(h.$$.fragment,a)},m(a,v){l(a,o,v),l(a,p,v),_(h,a,v),k=!0},p:R,i(a){k||(g(h.$$.fragment,a),k=!0)},o(a){T(h.$$.fragment,a),k=!1},d(a){a&&(r(o),r(p)),b(h,a)}}}function qs(w){let o,y=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){o=i("p"),o.innerHTML=y},l(p){o=c(p,"P",{"data-svelte-h":!0}),m(o)!=="svelte-fincs2"&&(o.innerHTML=y)},m(p,h){l(p,o,h)},p:R,d(p){p&&r(o)}}}function Cs(w){let o,y="Example:",p,h,k;return h=new Dt({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, ProphetNetModel

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;microsoft/prophetnet-large-uncased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ProphetNetModel.from_pretrained(<span class="hljs-string">&quot;microsoft/prophetnet-large-uncased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>input_ids = tokenizer(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;Studies have been shown that owning a dog is good for you&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>
<span class="hljs-meta">... </span>).input_ids  <span class="hljs-comment"># Batch size 1</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>decoder_input_ids = tokenizer(<span class="hljs-string">&quot;Studies show that&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>).input_ids  <span class="hljs-comment"># Batch size 1</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)

<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_states = outputs.last_hidden_state  <span class="hljs-comment"># main stream hidden states</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_states_ngram = outputs.last_hidden_state_ngram  <span class="hljs-comment"># predict hidden states</span>`,wrap:!1}}),{c(){o=i("p"),o.textContent=y,p=n(),u(h.$$.fragment)},l(a){o=c(a,"P",{"data-svelte-h":!0}),m(o)!=="svelte-11lpom8"&&(o.textContent=y),p=s(a),f(h.$$.fragment,a)},m(a,v){l(a,o,v),l(a,p,v),_(h,a,v),k=!0},p:R,i(a){k||(g(h.$$.fragment,a),k=!0)},o(a){T(h.$$.fragment,a),k=!1},d(a){a&&(r(o),r(p)),b(h,a)}}}function Fs(w){let o,y=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){o=i("p"),o.innerHTML=y},l(p){o=c(p,"P",{"data-svelte-h":!0}),m(o)!=="svelte-fincs2"&&(o.innerHTML=y)},m(p,h){l(p,o,h)},p:R,d(p){p&&r(o)}}}function js(w){let o,y="Example:",p,h,k;return h=new Dt({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, ProphetNetEncoder
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;microsoft/prophetnet-large-uncased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ProphetNetEncoder.from_pretrained(<span class="hljs-string">&quot;patrickvonplaten/prophetnet-large-uncased-standalone&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;Hello, my dog is cute&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_states = outputs.last_hidden_state`,wrap:!1}}),{c(){o=i("p"),o.textContent=y,p=n(),u(h.$$.fragment)},l(a){o=c(a,"P",{"data-svelte-h":!0}),m(o)!=="svelte-11lpom8"&&(o.textContent=y),p=s(a),f(h.$$.fragment,a)},m(a,v){l(a,o,v),l(a,p,v),_(h,a,v),k=!0},p:R,i(a){k||(g(h.$$.fragment,a),k=!0)},o(a){T(h.$$.fragment,a),k=!1},d(a){a&&(r(o),r(p)),b(h,a)}}}function Js(w){let o,y=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){o=i("p"),o.innerHTML=y},l(p){o=c(p,"P",{"data-svelte-h":!0}),m(o)!=="svelte-fincs2"&&(o.innerHTML=y)},m(p,h){l(p,o,h)},p:R,d(p){p&&r(o)}}}function Us(w){let o,y="Example:",p,h,k;return h=new Dt({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, ProphetNetDecoder
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;microsoft/prophetnet-large-uncased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ProphetNetDecoder.from_pretrained(<span class="hljs-string">&quot;microsoft/prophetnet-large-uncased&quot;</span>, add_cross_attention=<span class="hljs-literal">False</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;Hello, my dog is cute&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_states = outputs.last_hidden_state`,wrap:!1}}),{c(){o=i("p"),o.textContent=y,p=n(),u(h.$$.fragment)},l(a){o=c(a,"P",{"data-svelte-h":!0}),m(o)!=="svelte-11lpom8"&&(o.textContent=y),p=s(a),f(h.$$.fragment,a)},m(a,v){l(a,o,v),l(a,p,v),_(h,a,v),k=!0},p:R,i(a){k||(g(h.$$.fragment,a),k=!0)},o(a){T(h.$$.fragment,a),k=!1},d(a){a&&(r(o),r(p)),b(h,a)}}}function Is(w){let o,y=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){o=i("p"),o.innerHTML=y},l(p){o=c(p,"P",{"data-svelte-h":!0}),m(o)!=="svelte-fincs2"&&(o.innerHTML=y)},m(p,h){l(p,o,h)},p:R,d(p){p&&r(o)}}}function Ls(w){let o,y="Example:",p,h,k;return h=new Dt({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, ProphetNetForConditionalGeneration

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;microsoft/prophetnet-large-uncased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ProphetNetForConditionalGeneration.from_pretrained(<span class="hljs-string">&quot;microsoft/prophetnet-large-uncased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>input_ids = tokenizer(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;Studies have been shown that owning a dog is good for you&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>
<span class="hljs-meta">... </span>).input_ids  <span class="hljs-comment"># Batch size 1</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>decoder_input_ids = tokenizer(<span class="hljs-string">&quot;Studies show that&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>).input_ids  <span class="hljs-comment"># Batch size 1</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)

<span class="hljs-meta">&gt;&gt;&gt; </span>logits_next_token = outputs.logits  <span class="hljs-comment"># logits to predict next token as usual</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>logits_ngram_next_tokens = outputs.logits_ngram  <span class="hljs-comment"># logits to predict 2nd, 3rd, ... next tokens</span>`,wrap:!1}}),{c(){o=i("p"),o.textContent=y,p=n(),u(h.$$.fragment)},l(a){o=c(a,"P",{"data-svelte-h":!0}),m(o)!=="svelte-11lpom8"&&(o.textContent=y),p=s(a),f(h.$$.fragment,a)},m(a,v){l(a,o,v),l(a,p,v),_(h,a,v),k=!0},p:R,i(a){k||(g(h.$$.fragment,a),k=!0)},o(a){T(h.$$.fragment,a),k=!1},d(a){a&&(r(o),r(p)),b(h,a)}}}function Os(w){let o,y=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){o=i("p"),o.innerHTML=y},l(p){o=c(p,"P",{"data-svelte-h":!0}),m(o)!=="svelte-fincs2"&&(o.innerHTML=y)},m(p,h){l(p,o,h)},p:R,d(p){p&&r(o)}}}function Gs(w){let o,y="Example:",p,h,k;return h=new Dt({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Ub2tlbml6ZXIlMkMlMjBQcm9waGV0TmV0Rm9yQ2F1c2FsTE0lMEFpbXBvcnQlMjB0b3JjaCUwQSUwQXRva2VuaXplciUyMCUzRCUyMEF1dG9Ub2tlbml6ZXIuZnJvbV9wcmV0cmFpbmVkKCUyMm1pY3Jvc29mdCUyRnByb3BoZXRuZXQtbGFyZ2UtdW5jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMFByb3BoZXROZXRGb3JDYXVzYWxMTS5mcm9tX3ByZXRyYWluZWQoJTIybWljcm9zb2Z0JTJGcHJvcGhldG5ldC1sYXJnZS11bmNhc2VkJTIyKSUwQWFzc2VydCUyMG1vZGVsLmNvbmZpZy5pc19kZWNvZGVyJTJDJTIwZiUyMiU3Qm1vZGVsLl9fY2xhc3NfXyU3RCUyMGhhcyUyMHRvJTIwYmUlMjBjb25maWd1cmVkJTIwYXMlMjBhJTIwZGVjb2Rlci4lMjIlMEFpbnB1dHMlMjAlM0QlMjB0b2tlbml6ZXIoJTIySGVsbG8lMkMlMjBteSUyMGRvZyUyMGlzJTIwY3V0ZSUyMiUyQyUyMHJldHVybl90ZW5zb3JzJTNEJTIycHQlMjIpJTBBb3V0cHV0cyUyMCUzRCUyMG1vZGVsKCoqaW5wdXRzKSUwQSUwQWxvZ2l0cyUyMCUzRCUyMG91dHB1dHMubG9naXRzJTBBJTBBJTIzJTIwTW9kZWwlMjBjYW4lMjBhbHNvJTIwYmUlMjB1c2VkJTIwd2l0aCUyMEVuY29kZXJEZWNvZGVyJTIwZnJhbWV3b3JrJTBBZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEJlcnRUb2tlbml6ZXIlMkMlMjBFbmNvZGVyRGVjb2Rlck1vZGVsJTJDJTIwQXV0b1Rva2VuaXplciUwQWltcG9ydCUyMHRvcmNoJTBBJTBBdG9rZW5pemVyX2VuYyUyMCUzRCUyMEJlcnRUb2tlbml6ZXIuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZS1iZXJ0JTJGYmVydC1sYXJnZS11bmNhc2VkJTIyKSUwQXRva2VuaXplcl9kZWMlMjAlM0QlMjBBdXRvVG9rZW5pemVyLmZyb21fcHJldHJhaW5lZCglMjJtaWNyb3NvZnQlMkZwcm9waGV0bmV0LWxhcmdlLXVuY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBFbmNvZGVyRGVjb2Rlck1vZGVsLmZyb21fZW5jb2Rlcl9kZWNvZGVyX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIyZ29vZ2xlLWJlcnQlMkZiZXJ0LWxhcmdlLXVuY2FzZWQlMjIlMkMlMjAlMjJtaWNyb3NvZnQlMkZwcm9waGV0bmV0LWxhcmdlLXVuY2FzZWQlMjIlMEEpJTBBJTBBQVJUSUNMRSUyMCUzRCUyMCglMEElMjAlMjAlMjAlMjAlMjJ0aGUlMjB1cyUyMHN0YXRlJTIwZGVwYXJ0bWVudCUyMHNhaWQlMjB3ZWRuZXNkYXklMjBpdCUyMGhhZCUyMHJlY2VpdmVkJTIwbm8lMjAlMjIlMEElMjAlMjAlMjAlMjAlMjJmb3JtYWwlMjB3b3JkJTIwZnJvbSUyMGJvbGl2aWElMjB0aGF0JTIwaXQlMjB3YXMlMjBleHBlbGxpbmclMjB0aGUlMjB1cyUyMGFtYmFzc2Fkb3IlMjB0aGVyZSUyMCUyMiUwQSUyMCUyMCUyMCUyMCUyMmJ1dCUyMHNhaWQlMjB0aGUlMjBjaGFyZ2VzJTIwbWFkZSUyMGFnYWluc3QlMjBoaW0lMjBhcmUlMjAlNjAlNjAlMjBiYXNlbGVzcyUyMC4lMjIlMEEpJTBBaW5wdXRfaWRzJTIwJTNEJTIwdG9rZW5pemVyX2VuYyhBUlRJQ0xFJTJDJTIwcmV0dXJuX3RlbnNvcnMlM0QlMjJwdCUyMikuaW5wdXRfaWRzJTBBbGFiZWxzJTIwJTNEJTIwdG9rZW5pemVyX2RlYyglMEElMjAlMjAlMjAlMjAlMjJ1cyUyMHJlamVjdHMlMjBjaGFyZ2VzJTIwYWdhaW5zdCUyMGl0cyUyMGFtYmFzc2Fkb3IlMjBpbiUyMGJvbGl2aWElMjIlMkMlMjByZXR1cm5fdGVuc29ycyUzRCUyMnB0JTIyJTBBKS5pbnB1dF9pZHMlMEFvdXRwdXRzJTIwJTNEJTIwbW9kZWwoaW5wdXRfaWRzJTNEaW5wdXRfaWRzJTJDJTIwZGVjb2Rlcl9pbnB1dF9pZHMlM0RsYWJlbHMlNUIlM0ElMkMlMjAlM0EtMSU1RCUyQyUyMGxhYmVscyUzRGxhYmVscyU1QiUzQSUyQyUyMDElM0ElNUQpJTBBJTBBbG9zcyUyMCUzRCUyMG91dHB1dHMubG9zcw==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, ProphetNetForCausalLM
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;microsoft/prophetnet-large-uncased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ProphetNetForCausalLM.from_pretrained(<span class="hljs-string">&quot;microsoft/prophetnet-large-uncased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">assert</span> model.config.is_decoder, <span class="hljs-string">f&quot;<span class="hljs-subst">{model.__class__}</span> has to be configured as a decoder.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;Hello, my dog is cute&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Model can also be used with EncoderDecoder framework</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> BertTokenizer, EncoderDecoderModel, AutoTokenizer
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer_enc = BertTokenizer.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-large-uncased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer_dec = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;microsoft/prophetnet-large-uncased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = EncoderDecoderModel.from_encoder_decoder_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;google-bert/bert-large-uncased&quot;</span>, <span class="hljs-string">&quot;microsoft/prophetnet-large-uncased&quot;</span>
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>ARTICLE = (
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;the us state department said wednesday it had received no &quot;</span>
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;formal word from bolivia that it was expelling the us ambassador there &quot;</span>
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;but said the charges made against him are \`\` baseless .&quot;</span>
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>input_ids = tokenizer_enc(ARTICLE, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>).input_ids
<span class="hljs-meta">&gt;&gt;&gt; </span>labels = tokenizer_dec(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;us rejects charges against its ambassador in bolivia&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>
<span class="hljs-meta">... </span>).input_ids
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(input_ids=input_ids, decoder_input_ids=labels[:, :-<span class="hljs-number">1</span>], labels=labels[:, <span class="hljs-number">1</span>:])

<span class="hljs-meta">&gt;&gt;&gt; </span>loss = outputs.loss`,wrap:!1}}),{c(){o=i("p"),o.textContent=y,p=n(),u(h.$$.fragment)},l(a){o=c(a,"P",{"data-svelte-h":!0}),m(o)!=="svelte-11lpom8"&&(o.textContent=y),p=s(a),f(h.$$.fragment,a)},m(a,v){l(a,o,v),l(a,p,v),_(h,a,v),k=!0},p:R,i(a){k||(g(h.$$.fragment,a),k=!0)},o(a){T(h.$$.fragment,a),k=!1},d(a){a&&(r(o),r(p)),b(h,a)}}}function Zs(w){let o,y,p,h,k,a,v,jn='<a href="https://huggingface.co/models?filter=prophetnet"><img alt="Models" src="https://img.shields.io/badge/All_model_pages-prophetnet-blueviolet"/></a> <a href="https://huggingface.co/spaces/docs-demos/prophetnet-large-uncased"><img alt="Spaces" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue"/></a>',Qt,ye,Yt,ke,Jn=`The ProphetNet model was proposed in <a href="https://arxiv.org/abs/2001.04063" rel="nofollow">ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training,</a> by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei
Zhang, Ming Zhou on 13 Jan, 2020.`,Kt,ve,Un=`ProphetNet is an encoder-decoder model and can predict n-future tokens for “ngram” language modeling instead of just
the next token.`,eo,we,In="The abstract from the paper is the following:",to,Me,Ln=`<em>In this paper, we present a new sequence-to-sequence pretraining model called ProphetNet, which introduces a novel
self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism. Instead of
the optimization of one-step ahead prediction in traditional sequence-to-sequence model, the ProphetNet is optimized by
n-step ahead prediction which predicts the next n tokens simultaneously based on previous context tokens at each time
step. The future n-gram prediction explicitly encourages the model to plan for the future tokens and prevent
overfitting on strong local correlations. We pre-train ProphetNet using a base scale dataset (16GB) and a large scale
dataset (160GB) respectively. Then we conduct experiments on CNN/DailyMail, Gigaword, and SQuAD 1.1 benchmarks for
abstractive summarization and question generation tasks. Experimental results show that ProphetNet achieves new
state-of-the-art results on all these datasets compared to the models using the same scale pretraining corpus.</em>`,oo,Ne,On='The Authors’ code can be found <a href="https://github.com/microsoft/ProphetNet" rel="nofollow">here</a>.',no,$e,so,ze,Gn=`<li>ProphetNet is a model with absolute position embeddings so it’s usually advised to pad the inputs on the right rather than
the left.</li> <li>The model architecture is based on the original Transformer, but replaces the “standard” self-attention mechanism in the decoder by a a main self-attention mechanism and a self and n-stream (predict) self-attention mechanism.</li>`,ro,Pe,ao,xe,Zn='<li><a href="../tasks/language_modeling">Causal language modeling task guide</a></li> <li><a href="../tasks/translation">Translation task guide</a></li> <li><a href="../tasks/summarization">Summarization task guide</a></li>',io,qe,co,V,Ce,qo,pt,Wn=`This is the configuration class to store the configuration of a <a href="/docs/transformers/main/en/model_doc/prophetnet#transformers.ProphetNetModel">ProphetNetModel</a>. It is used to instantiate a
ProphetNet model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the ProphetNet
<a href="https://huggingface.co/microsoft/prophetnet-large-uncased" rel="nofollow">microsoft/prophetnet-large-uncased</a> architecture.`,Co,ht,Hn=`Configuration objects inherit from <a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> and can be used to control the model outputs. Read the
documentation from <a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> for more information.`,lo,Fe,po,z,je,Fo,mt,Sn="Construct a ProphetNetTokenizer. Based on WordPiece.",jo,ut,Vn=`This tokenizer inherits from <a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a> which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.`,Jo,E,Je,Uo,ft,Bn=`Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A BERT sequence has the following format:`,Io,_t,Rn="<li>single sequence: <code>[CLS] X [SEP]</code></li> <li>pair of sequences: <code>[CLS] A [SEP] B [SEP]</code></li>",Lo,oe,Ue,Oo,gt,En="Converts a sequence of tokens (string) in a single string.",Go,U,Ie,Zo,Tt,Dn="Create a mask from the two sequences passed to be used in a sequence-pair classification task. A ProphetNet",Wo,ne,Ho,bt,Xn="If <code>token_ids_1</code> is <code>None</code>, this method only returns the first portion of the mask (0s).",So,se,Le,Vo,yt,An=`Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer <code>prepare_for_model</code> method.`,ho,Oe,mo,A,Ge,Bo,kt,Qn="Base class for sequence-to-sequence language models outputs.",uo,Q,Ze,Ro,vt,Yn=`Base class for model encoder’s outputs that also contains : pre-computed hidden states that can speed up sequential
decoding.`,fo,Y,We,Eo,wt,Kn="Base class for model’s outputs that may also contain a past key/values (to speed up sequential decoding).",_o,K,He,Do,Mt,es="Base class for model’s outputs that may also contain a past key/values (to speed up sequential decoding).",go,Se,To,C,Ve,Xo,Nt,ts=`The bare ProphetNet Model outputting raw hidden-states without any specific head on top.
This model inherits from <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,Ao,$t,os=`Original ProphetNet code can be found <a href="https://github.com/microsoft/ProphetNet" rel="nofollow">here</a>. Checkpoints were converted
from original Fairseq checkpoints. For more information on the checkpoint conversion, please take a look at the
file <code>convert_prophetnet_original_pytorch_checkpoint_to_pytorch.py</code>.`,Qo,zt,ns=`This model is a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matters related to general usage and
behavior.`,Yo,I,Be,Ko,Pt,ss='The <a href="/docs/transformers/main/en/model_doc/prophetnet#transformers.ProphetNetModel">ProphetNetModel</a> forward method, overrides the <code>__call__</code> special method.',en,re,tn,ae,bo,Re,yo,P,Ee,on,xt,rs=`The standalone encoder part of the ProphetNetModel.
This model inherits from <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,nn,qt,as=`Original ProphetNet code can be found <a href="https://github.com/microsoft/ProphetNet" rel="nofollow">here</a>. Checkpoints were converted
from original Fairseq checkpoints. For more information on the checkpoint conversion, please take a look at the
file <code>convert_prophetnet_original_pytorch_checkpoint_to_pytorch.py</code>.`,sn,Ct,ds=`This model is a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matters related to general usage and
behavior.`,rn,Ft,is=`word_embeddings  (<code>torch.nn.Embeddings</code> of shape <code>(config.vocab_size, config.hidden_size)</code>, <em>optional</em>):
The word embedding parameters. This can be used to initialize <a href="/docs/transformers/main/en/model_doc/prophetnet#transformers.ProphetNetEncoder">ProphetNetEncoder</a> with pre-defined word
embeddings instead of randomly initialized word embeddings.`,an,L,De,dn,jt,cs='The <a href="/docs/transformers/main/en/model_doc/prophetnet#transformers.ProphetNetEncoder">ProphetNetEncoder</a> forward method, overrides the <code>__call__</code> special method.',cn,de,ln,ie,ko,Xe,vo,x,Ae,pn,Jt,ls=`The standalone decoder part of the ProphetNetModel.
This model inherits from <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,hn,Ut,ps=`Original ProphetNet code can be found <a href="https://github.com/microsoft/ProphetNet" rel="nofollow">here</a>. Checkpoints were converted
from original Fairseq checkpoints. For more information on the checkpoint conversion, please take a look at the
file <code>convert_prophetnet_original_pytorch_checkpoint_to_pytorch.py</code>.`,mn,It,hs=`This model is a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matters related to general usage and
behavior.`,un,Lt,ms=`word_embeddings  (<code>torch.nn.Embeddings</code> of shape <code>(config.vocab_size, config.hidden_size)</code>, <em>optional</em>):
The word embedding parameters. This can be used to initialize <a href="/docs/transformers/main/en/model_doc/prophetnet#transformers.ProphetNetEncoder">ProphetNetEncoder</a> with pre-defined word
embeddings instead of randomly initialized word embeddings.`,fn,O,Qe,_n,Ot,us='The <a href="/docs/transformers/main/en/model_doc/prophetnet#transformers.ProphetNetDecoder">ProphetNetDecoder</a> forward method, overrides the <code>__call__</code> special method.',gn,ce,Tn,le,wo,Ye,Mo,F,Ke,bn,Gt,fs=`The ProphetNet Model with a language modeling head. Can be used for sequence generation tasks.
This model inherits from <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,yn,Zt,_s=`Original ProphetNet code can be found <a href="https://github.com/microsoft/ProphetNet" rel="nofollow">here</a>. Checkpoints were converted
from original Fairseq checkpoints. For more information on the checkpoint conversion, please take a look at the
file <code>convert_prophetnet_original_pytorch_checkpoint_to_pytorch.py</code>.`,kn,Wt,gs=`This model is a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matters related to general usage and
behavior.`,vn,G,et,wn,Ht,Ts='The <a href="/docs/transformers/main/en/model_doc/prophetnet#transformers.ProphetNetForConditionalGeneration">ProphetNetForConditionalGeneration</a> forward method, overrides the <code>__call__</code> special method.',Mn,pe,Nn,he,No,tt,$o,j,ot,$n,St,bs=`The standalone decoder part of the ProphetNetModel with a lm head on top. The model can be used for causal language modeling.
This model inherits from <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,zn,Vt,ys=`Original ProphetNet code can be found <a href="https://github.com/microsoft/ProphetNet" rel="nofollow">here</a>. Checkpoints were converted
from original Fairseq checkpoints. For more information on the checkpoint conversion, please take a look at the
file <code>convert_prophetnet_original_pytorch_checkpoint_to_pytorch.py</code>.`,Pn,Bt,ks=`This model is a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matters related to general usage and
behavior.`,xn,Z,nt,qn,Rt,vs='The <a href="/docs/transformers/main/en/model_doc/prophetnet#transformers.ProphetNetForCausalLM">ProphetNetForCausalLM</a> forward method, overrides the <code>__call__</code> special method.',Cn,me,Fn,ue,zo,st,Po,Xt,xo;return k=new S({props:{title:"ProphetNet",local:"prophetnet",headingTag:"h1"}}),ye=new S({props:{title:"Overview",local:"overview",headingTag:"h2"}}),$e=new S({props:{title:"Usage tips",local:"usage-tips",headingTag:"h2"}}),Pe=new S({props:{title:"Resources",local:"resources",headingTag:"h2"}}),qe=new S({props:{title:"ProphetNetConfig",local:"transformers.ProphetNetConfig",headingTag:"h2"}}),Ce=new $({props:{name:"class transformers.ProphetNetConfig",anchor:"transformers.ProphetNetConfig",parameters:[{name:"activation_dropout",val:": Optional = 0.1"},{name:"activation_function",val:": Union = 'gelu'"},{name:"vocab_size",val:": Optional = 30522"},{name:"hidden_size",val:": Optional = 1024"},{name:"encoder_ffn_dim",val:": Optional = 4096"},{name:"num_encoder_layers",val:": Optional = 12"},{name:"num_encoder_attention_heads",val:": Optional = 16"},{name:"decoder_ffn_dim",val:": Optional = 4096"},{name:"num_decoder_layers",val:": Optional = 12"},{name:"num_decoder_attention_heads",val:": Optional = 16"},{name:"attention_dropout",val:": Optional = 0.1"},{name:"dropout",val:": Optional = 0.1"},{name:"max_position_embeddings",val:": Optional = 512"},{name:"init_std",val:": Optional = 0.02"},{name:"is_encoder_decoder",val:": Optional = True"},{name:"add_cross_attention",val:": Optional = True"},{name:"decoder_start_token_id",val:": Optional = 0"},{name:"ngram",val:": Optional = 2"},{name:"num_buckets",val:": Optional = 32"},{name:"relative_max_distance",val:": Optional = 128"},{name:"disable_ngram_loss",val:": Optional = False"},{name:"eps",val:": Optional = 0.0"},{name:"use_cache",val:": Optional = True"},{name:"pad_token_id",val:": Optional = 0"},{name:"bos_token_id",val:": Optional = 1"},{name:"eos_token_id",val:": Optional = 2"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.ProphetNetConfig.activation_dropout",description:`<strong>activation_dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout ratio for activations inside the fully connected layer.`,name:"activation_dropout"},{anchor:"transformers.ProphetNetConfig.activation_function",description:`<strong>activation_function</strong> (<code>str</code> or <code>function</code>, <em>optional</em>, defaults to <code>&quot;gelu&quot;</code>) &#x2014;
The non-linear activation function (function or string) in the encoder and pooler. If string, <code>&quot;gelu&quot;</code>,
<code>&quot;relu&quot;</code>, <code>&quot;silu&quot;</code> and <code>&quot;gelu_new&quot;</code> are supported.`,name:"activation_function"},{anchor:"transformers.ProphetNetConfig.vocab_size",description:`<strong>vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 30522) &#x2014;
Vocabulary size of the ProphetNET model. Defines the number of different tokens that can be represented by
the <code>inputs_ids</code> passed when calling <a href="/docs/transformers/main/en/model_doc/prophetnet#transformers.ProphetNetModel">ProphetNetModel</a>.`,name:"vocab_size"},{anchor:"transformers.ProphetNetConfig.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1024) &#x2014;
Dimensionality of the layers and the pooler layer.`,name:"hidden_size"},{anchor:"transformers.ProphetNetConfig.encoder_ffn_dim",description:`<strong>encoder_ffn_dim</strong> (<code>int</code>, <em>optional</em>, defaults to 4096) &#x2014;
Dimensionality of the &#x201C;intermediate&#x201D; (often named feed-forward) layer in decoder.`,name:"encoder_ffn_dim"},{anchor:"transformers.ProphetNetConfig.num_encoder_layers",description:`<strong>num_encoder_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
Number of encoder layers.`,name:"num_encoder_layers"},{anchor:"transformers.ProphetNetConfig.num_encoder_attention_heads",description:`<strong>num_encoder_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 16) &#x2014;
Number of attention heads for each attention layer in the Transformer encoder.`,name:"num_encoder_attention_heads"},{anchor:"transformers.ProphetNetConfig.decoder_ffn_dim",description:`<strong>decoder_ffn_dim</strong> (<code>int</code>, <em>optional</em>, defaults to 4096) &#x2014;
Dimensionality of the <code>intermediate</code> (often named feed-forward) layer in decoder.`,name:"decoder_ffn_dim"},{anchor:"transformers.ProphetNetConfig.num_decoder_layers",description:`<strong>num_decoder_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
Number of decoder layers.`,name:"num_decoder_layers"},{anchor:"transformers.ProphetNetConfig.num_decoder_attention_heads",description:`<strong>num_decoder_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 16) &#x2014;
Number of attention heads for each attention layer in the Transformer decoder.`,name:"num_decoder_attention_heads"},{anchor:"transformers.ProphetNetConfig.attention_dropout",description:`<strong>attention_dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout ratio for the attention probabilities.`,name:"attention_dropout"},{anchor:"transformers.ProphetNetConfig.dropout",description:`<strong>dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.`,name:"dropout"},{anchor:"transformers.ProphetNetConfig.max_position_embeddings",description:`<strong>max_position_embeddings</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).`,name:"max_position_embeddings"},{anchor:"transformers.ProphetNetConfig.init_std",description:`<strong>init_std</strong> (<code>float</code>, <em>optional</em>, defaults to 0.02) &#x2014;
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.`,name:"init_std"},{anchor:"transformers.ProphetNetConfig.add_cross_attention",description:`<strong>add_cross_attention</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether cross-attention layers should be added to the model.`,name:"add_cross_attention"},{anchor:"transformers.ProphetNetConfig.is_encoder_decoder",description:`<strong>is_encoder_decoder</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether this is an encoder/decoder model.`,name:"is_encoder_decoder"},{anchor:"transformers.ProphetNetConfig.pad_token_id",description:`<strong>pad_token_id</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
Padding token id.`,name:"pad_token_id"},{anchor:"transformers.ProphetNetConfig.bos_token_id",description:`<strong>bos_token_id</strong> (<code>int</code>, <em>optional</em>, defaults to 0) &#x2014;
Beginning of stream token id.`,name:"bos_token_id"},{anchor:"transformers.ProphetNetConfig.eos_token_id",description:`<strong>eos_token_id</strong> (<code>int</code>, <em>optional</em>, defaults to 2) &#x2014;
End of stream token id.`,name:"eos_token_id"},{anchor:"transformers.ProphetNetConfig.ngram",description:`<strong>ngram</strong> (<code>int</code>, <em>optional</em>, defaults to 2) &#x2014;
Number of future tokens to predict. Set to 1 to be same as traditional Language model to predict next first
token.`,name:"ngram"},{anchor:"transformers.ProphetNetConfig.num_buckets",description:`<strong>num_buckets</strong> (<code>int</code>, <em>optional</em>, defaults to 32) &#x2014;
The number of buckets to use for each attention layer. This is for relative position calculation. See the
[T5 paper](see <a href="https://arxiv.org/abs/1910.10683" rel="nofollow">https://arxiv.org/abs/1910.10683</a>) for more details.`,name:"num_buckets"},{anchor:"transformers.ProphetNetConfig.relative_max_distance",description:`<strong>relative_max_distance</strong> (<code>int</code>, <em>optional</em>, defaults to 128) &#x2014;
Relative distances greater than this number will be put into the last same bucket. This is for relative
position calculation. See the [T5 paper](see <a href="https://arxiv.org/abs/1910.10683" rel="nofollow">https://arxiv.org/abs/1910.10683</a>) for more details.`,name:"relative_max_distance"},{anchor:"transformers.ProphetNetConfig.disable_ngram_loss",description:`<strong>disable_ngram_loss</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether be trained predicting only the next first token.`,name:"disable_ngram_loss"},{anchor:"transformers.ProphetNetConfig.eps",description:`<strong>eps</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
Controls the <code>epsilon</code> parameter value for label smoothing in the loss calculation. If set to 0, no label
smoothing is performed.`,name:"eps"},{anchor:"transformers.ProphetNetConfig.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not the model should return the last key/values attentions (not used by all models).`,name:"use_cache"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/prophetnet/configuration_prophetnet.py#L26"}}),Fe=new S({props:{title:"ProphetNetTokenizer",local:"transformers.ProphetNetTokenizer",headingTag:"h2"}}),je=new $({props:{name:"class transformers.ProphetNetTokenizer",anchor:"transformers.ProphetNetTokenizer",parameters:[{name:"vocab_file",val:": str"},{name:"do_lower_case",val:": Optional = True"},{name:"do_basic_tokenize",val:": Optional = True"},{name:"never_split",val:": Optional = None"},{name:"unk_token",val:": Optional = '[UNK]'"},{name:"sep_token",val:": Optional = '[SEP]'"},{name:"x_sep_token",val:": Optional = '[X_SEP]'"},{name:"pad_token",val:": Optional = '[PAD]'"},{name:"mask_token",val:": Optional = '[MASK]'"},{name:"tokenize_chinese_chars",val:": Optional = True"},{name:"strip_accents",val:": Optional = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.ProphetNetTokenizer.vocab_file",description:`<strong>vocab_file</strong> (<code>str</code>) &#x2014;
File containing the vocabulary.`,name:"vocab_file"},{anchor:"transformers.ProphetNetTokenizer.do_lower_case",description:`<strong>do_lower_case</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to lowercase the input when tokenizing.`,name:"do_lower_case"},{anchor:"transformers.ProphetNetTokenizer.do_basic_tokenize",description:`<strong>do_basic_tokenize</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to do basic tokenization before WordPiece.`,name:"do_basic_tokenize"},{anchor:"transformers.ProphetNetTokenizer.never_split",description:`<strong>never_split</strong> (<code>Iterable</code>, <em>optional</em>) &#x2014;
Collection of tokens which will never be split during tokenization. Only has an effect when
<code>do_basic_tokenize=True</code>`,name:"never_split"},{anchor:"transformers.ProphetNetTokenizer.unk_token",description:`<strong>unk_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[UNK]&quot;</code>) &#x2014;
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.`,name:"unk_token"},{anchor:"transformers.ProphetNetTokenizer.sep_token",description:`<strong>sep_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[SEP]&quot;</code>) &#x2014;
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.`,name:"sep_token"},{anchor:"transformers.ProphetNetTokenizer.x_sep_token",description:`<strong>x_sep_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[X_SEP]&quot;</code>) &#x2014;
Special second separator token, which can be generated by <a href="/docs/transformers/main/en/model_doc/prophetnet#transformers.ProphetNetForConditionalGeneration">ProphetNetForConditionalGeneration</a>. It is
used to separate bullet-point like sentences in summarization, <em>e.g.</em>.`,name:"x_sep_token"},{anchor:"transformers.ProphetNetTokenizer.pad_token",description:`<strong>pad_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[PAD]&quot;</code>) &#x2014;
The token used for padding, for example when batching sequences of different lengths.`,name:"pad_token"},{anchor:"transformers.ProphetNetTokenizer.mask_token",description:`<strong>mask_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[MASK]&quot;</code>) &#x2014;
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.`,name:"mask_token"},{anchor:"transformers.ProphetNetTokenizer.tokenize_chinese_chars",description:`<strong>tokenize_chinese_chars</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to tokenize Chinese characters.</p>
<p>This should likely be deactivated for Japanese (see this
<a href="https://github.com/huggingface/transformers/issues/328" rel="nofollow">issue</a>).`,name:"tokenize_chinese_chars"},{anchor:"transformers.ProphetNetTokenizer.strip_accents",description:`<strong>strip_accents</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for <code>lowercase</code> (as in the original BERT).`,name:"strip_accents"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/prophetnet/tokenization_prophetnet.py#L271"}}),Je=new $({props:{name:"build_inputs_with_special_tokens",anchor:"transformers.ProphetNetTokenizer.build_inputs_with_special_tokens",parameters:[{name:"token_ids_0",val:": List"},{name:"token_ids_1",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.ProphetNetTokenizer.build_inputs_with_special_tokens.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
List of IDs to which the special tokens will be added.`,name:"token_ids_0"},{anchor:"transformers.ProphetNetTokenizer.build_inputs_with_special_tokens.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/prophetnet/tokenization_prophetnet.py#L477",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>List of <a href="../glossary#input-ids">input IDs</a> with the appropriate special tokens.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[int]</code></p>
`}}),Ue=new $({props:{name:"convert_tokens_to_string",anchor:"transformers.ProphetNetTokenizer.convert_tokens_to_string",parameters:[{name:"tokens",val:": str"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/prophetnet/tokenization_prophetnet.py#L394"}}),Ie=new $({props:{name:"create_token_type_ids_from_sequences",anchor:"transformers.ProphetNetTokenizer.create_token_type_ids_from_sequences",parameters:[{name:"token_ids_0",val:": List"},{name:"token_ids_1",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.ProphetNetTokenizer.create_token_type_ids_from_sequences.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
List of IDs.`,name:"token_ids_0"},{anchor:"transformers.ProphetNetTokenizer.create_token_type_ids_from_sequences.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/prophetnet/tokenization_prophetnet.py#L429",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>List of <a href="../glossary#token-type-ids">token type IDs</a> according to the given sequence(s).</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[int]</code></p>
`}}),ne=new Et({props:{anchor:"transformers.ProphetNetTokenizer.create_token_type_ids_from_sequences.example",$$slots:{default:[xs]},$$scope:{ctx:w}}}),Le=new $({props:{name:"get_special_tokens_mask",anchor:"transformers.ProphetNetTokenizer.get_special_tokens_mask",parameters:[{name:"token_ids_0",val:": List"},{name:"token_ids_1",val:": Optional = None"},{name:"already_has_special_tokens",val:": Optional = False"}],parametersDescription:[{anchor:"transformers.ProphetNetTokenizer.get_special_tokens_mask.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
List of IDs.`,name:"token_ids_0"},{anchor:"transformers.ProphetNetTokenizer.get_special_tokens_mask.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"},{anchor:"transformers.ProphetNetTokenizer.get_special_tokens_mask.already_has_special_tokens",description:`<strong>already_has_special_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not the token list is already formatted with special tokens for the model.`,name:"already_has_special_tokens"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/prophetnet/tokenization_prophetnet.py#L399",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[int]</code></p>
`}}),Oe=new S({props:{title:"ProphetNet specific outputs",local:"transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqLMOutput",headingTag:"h2"}}),Ge=new $({props:{name:"class transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqLMOutput",anchor:"transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqLMOutput",parameters:[{name:"loss",val:": Optional = None"},{name:"logits",val:": FloatTensor = None"},{name:"logits_ngram",val:": Optional = None"},{name:"past_key_values",val:": Optional = None"},{name:"decoder_hidden_states",val:": Optional = None"},{name:"decoder_ngram_hidden_states",val:": Optional = None"},{name:"decoder_attentions",val:": Optional = None"},{name:"decoder_ngram_attentions",val:": Optional = None"},{name:"cross_attentions",val:": Optional = None"},{name:"encoder_last_hidden_state",val:": Optional = None"},{name:"encoder_hidden_states",val:": Optional = None"},{name:"encoder_attentions",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqLMOutput.loss",description:`<strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) &#x2014;
Language modeling loss.`,name:"loss"},{anchor:"transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqLMOutput.logits",description:`<strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, decoder_sequence_length, config.vocab_size)</code>) &#x2014;
Prediction scores of the main stream language modeling head (scores for each vocabulary token before
SoftMax).`,name:"logits"},{anchor:"transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqLMOutput.logits_ngram",description:`<strong>logits_ngram</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, ngram * decoder_sequence_length, config.vocab_size)</code>) &#x2014;
Prediction scores of the predict stream language modeling head (scores for each vocabulary token before
SoftMax).`,name:"logits_ngram"},{anchor:"transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqLMOutput.past_key_values",description:`<strong>past_key_values</strong> (<code>List[torch.FloatTensor]</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) &#x2014;
List of <code>torch.FloatTensor</code> of length <code>config.n_layers</code>, with each tensor of shape <code>(2, batch_size, num_attn_heads, decoder_sequence_length, embed_size_per_head)</code>).</p>
<p>Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see <code>past_key_values</code> input) to speed up sequential decoding.`,name:"past_key_values"},{anchor:"transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqLMOutput.decoder_hidden_states",description:`<strong>decoder_hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) &#x2014;
Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, decoder_sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of main stream of the decoder at the output of each layer plus the initial embedding outputs.`,name:"decoder_hidden_states"},{anchor:"transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqLMOutput.decoder_ngram_hidden_states",description:`<strong>decoder_ngram_hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) &#x2014;
Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, ngram * decoder_sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the predict stream of the decoder at the output of each layer plus the initial embedding
outputs.`,name:"decoder_ngram_hidden_states"},{anchor:"transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqLMOutput.decoder_attentions",description:`<strong>decoder_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) &#x2014;
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_attn_heads, decoder_sequence_length, decoder_sequence_length)</code>.</p>
<p>Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.`,name:"decoder_attentions"},{anchor:"transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqLMOutput.decoder_ngram_attentions",description:`<strong>decoder_ngram_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) &#x2014;
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_attn_heads, decoder_sequence_length, decoder_sequence_length)</code>.</p>
<p>Attentions weights of the predict stream of the decoder, after the attention softmax, used to compute the
weighted average in the self-attention heads.`,name:"decoder_ngram_attentions"},{anchor:"transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqLMOutput.cross_attentions",description:`<strong>cross_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) &#x2014;
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_attn_heads, encoder_sequence_length, decoder_sequence_length)</code>.</p>
<p>Attentions weights of the cross-attention layer of the decoder, after the attention softmax, used to
compute the weighted average in the`,name:"cross_attentions"},{anchor:"transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqLMOutput.encoder_last_hidden_state",description:`<strong>encoder_last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, encoder_sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Sequence of hidden-states at the output of the last layer of the encoder of the model.`,name:"encoder_last_hidden_state"},{anchor:"transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqLMOutput.encoder_hidden_states",description:`<strong>encoder_hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) &#x2014;
Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, encoder_sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.`,name:"encoder_hidden_states"},{anchor:"transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqLMOutput.encoder_attentions",description:`<strong>encoder_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) &#x2014;
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_attn_heads, encoder_sequence_length, encoder_sequence_length)</code>. Attentions weights of the encoder, after the attention
softmax, used to compute the weighted average in the self-attention heads.`,name:"encoder_attentions"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/prophetnet/modeling_prophetnet.py#L247"}}),Ze=new $({props:{name:"class transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqModelOutput",anchor:"transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqModelOutput",parameters:[{name:"last_hidden_state",val:": FloatTensor"},{name:"last_hidden_state_ngram",val:": Optional = None"},{name:"past_key_values",val:": Optional = None"},{name:"decoder_hidden_states",val:": Optional = None"},{name:"decoder_ngram_hidden_states",val:": Optional = None"},{name:"decoder_attentions",val:": Optional = None"},{name:"decoder_ngram_attentions",val:": Optional = None"},{name:"cross_attentions",val:": Optional = None"},{name:"encoder_last_hidden_state",val:": Optional = None"},{name:"encoder_hidden_states",val:": Optional = None"},{name:"encoder_attentions",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqModelOutput.last_hidden_state",description:`<strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, decoder_sequence_length, hidden_size)</code>) &#x2014;
Sequence of main stream hidden-states at the output of the last layer of the decoder of the model.</p>
<p>If <code>past_key_values</code> is used only the last hidden-state of the sequences of shape <code>(batch_size, 1, hidden_size)</code> is output.`,name:"last_hidden_state"},{anchor:"transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqModelOutput.last_hidden_state_ngram",description:`<strong>last_hidden_state_ngram</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size,ngram * decoder_sequence_length, config.vocab_size)</code>, <em>optional</em>) &#x2014;
Sequence of predict stream hidden-states at the output of the last layer of the decoder of the model.`,name:"last_hidden_state_ngram"},{anchor:"transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqModelOutput.past_key_values",description:`<strong>past_key_values</strong> (<code>List[torch.FloatTensor]</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) &#x2014;
List of <code>torch.FloatTensor</code> of length <code>config.n_layers</code>, with each tensor of shape <code>(2, batch_size, num_attn_heads, decoder_sequence_length, embed_size_per_head)</code>).</p>
<p>Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see <code>past_key_values</code> input) to speed up sequential decoding.`,name:"past_key_values"},{anchor:"transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqModelOutput.decoder_hidden_states",description:`<strong>decoder_hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) &#x2014;
Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, decoder_sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of main stream of the decoder at the output of each layer plus the initial embedding outputs.`,name:"decoder_hidden_states"},{anchor:"transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqModelOutput.decoder_ngram_hidden_states",description:`<strong>decoder_ngram_hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) &#x2014;
Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, ngram * decoder_sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the predict stream of the decoder at the output of each layer plus the initial embedding
outputs.`,name:"decoder_ngram_hidden_states"},{anchor:"transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqModelOutput.decoder_attentions",description:`<strong>decoder_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) &#x2014;
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_attn_heads, decoder_sequence_length, decoder_sequence_length)</code>.</p>
<p>Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.`,name:"decoder_attentions"},{anchor:"transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqModelOutput.decoder_ngram_attentions",description:`<strong>decoder_ngram_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) &#x2014;
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_attn_heads, decoder_sequence_length, decoder_sequence_length)</code>.</p>
<p>Attentions weights of the predict stream of the decoder, after the attention softmax, used to compute the
weighted average in the`,name:"decoder_ngram_attentions"},{anchor:"transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqModelOutput.cross_attentions",description:`<strong>cross_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) &#x2014;
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_attn_heads, encoder_sequence_length, decoder_sequence_length)</code>.</p>
<p>Attentions weights of the cross-attention layer of the decoder, after the attention softmax, used to
compute the weighted average in the`,name:"cross_attentions"},{anchor:"transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqModelOutput.encoder_last_hidden_state",description:`<strong>encoder_last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, encoder_sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Sequence of hidden-states at the output of the last layer of the encoder of the model.`,name:"encoder_last_hidden_state"},{anchor:"transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqModelOutput.encoder_hidden_states",description:`<strong>encoder_hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) &#x2014;
Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, encoder_sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.`,name:"encoder_hidden_states"},{anchor:"transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqModelOutput.encoder_attentions",description:`<strong>encoder_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) &#x2014;
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_attn_heads, encoder_sequence_length, encoder_sequence_length)</code>.</p>
<p>Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.`,name:"encoder_attentions"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/prophetnet/modeling_prophetnet.py#L332"}}),We=new $({props:{name:"class transformers.models.prophetnet.modeling_prophetnet.ProphetNetDecoderModelOutput",anchor:"transformers.models.prophetnet.modeling_prophetnet.ProphetNetDecoderModelOutput",parameters:[{name:"last_hidden_state",val:": FloatTensor"},{name:"last_hidden_state_ngram",val:": Optional = None"},{name:"past_key_values",val:": Optional = None"},{name:"hidden_states",val:": Optional = None"},{name:"hidden_states_ngram",val:": Optional = None"},{name:"attentions",val:": Optional = None"},{name:"ngram_attentions",val:": Optional = None"},{name:"cross_attentions",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.models.prophetnet.modeling_prophetnet.ProphetNetDecoderModelOutput.last_hidden_state",description:`<strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, decoder_sequence_length, hidden_size)</code>) &#x2014;
Sequence of main stream hidden-states at the output of the last layer of the decoder of the model.</p>
<p>If <code>past_key_values</code> is used only the last hidden-state of the sequences of shape <code>(batch_size, 1, hidden_size)</code> is output.`,name:"last_hidden_state"},{anchor:"transformers.models.prophetnet.modeling_prophetnet.ProphetNetDecoderModelOutput.last_hidden_state_ngram",description:`<strong>last_hidden_state_ngram</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, ngram * decoder_sequence_length, config.vocab_size)</code>) &#x2014;
Sequence of predict stream hidden-states at the output of the last layer of the decoder of the model.`,name:"last_hidden_state_ngram"},{anchor:"transformers.models.prophetnet.modeling_prophetnet.ProphetNetDecoderModelOutput.past_key_values",description:`<strong>past_key_values</strong> (<code>List[torch.FloatTensor]</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) &#x2014;
List of <code>torch.FloatTensor</code> of length <code>config.n_layers</code>, with each tensor of shape <code>(2, batch_size, num_attn_heads, decoder_sequence_length, embed_size_per_head)</code>).</p>
<p>Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see <code>past_key_values</code> input) to speed up sequential decoding.`,name:"past_key_values"},{anchor:"transformers.models.prophetnet.modeling_prophetnet.ProphetNetDecoderModelOutput.hidden_states",description:`<strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) &#x2014;
Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, decoder_sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of main stream of the decoder at the output of each layer plus the initial embedding outputs.`,name:"hidden_states"},{anchor:"transformers.models.prophetnet.modeling_prophetnet.ProphetNetDecoderModelOutput.ngram_hidden_states",description:`<strong>ngram_hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) &#x2014;
Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, ngram * decoder_sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the predict stream of the decoder at the output of each layer plus the initial embedding
outputs.`,name:"ngram_hidden_states"},{anchor:"transformers.models.prophetnet.modeling_prophetnet.ProphetNetDecoderModelOutput.attentions",description:`<strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) &#x2014;
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_attn_heads, decoder_sequence_length, decoder_sequence_length)</code>.</p>
<p>Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.`,name:"attentions"},{anchor:"transformers.models.prophetnet.modeling_prophetnet.ProphetNetDecoderModelOutput.ngram_attentions",description:`<strong>ngram_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) &#x2014;
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_attn_heads, decoder_sequence_length, decoder_sequence_length)</code>.</p>
<p>Attentions weights of the predict stream of the decoder, after the attention softmax, used to compute the
weighted average in the`,name:"ngram_attentions"},{anchor:"transformers.models.prophetnet.modeling_prophetnet.ProphetNetDecoderModelOutput.cross_attentions",description:`<strong>cross_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) &#x2014;
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_attn_heads, encoder_sequence_length, decoder_sequence_length)</code>.</p>
<p>Attentions weights of the cross-attention layer of the decoder, after the attention softmax, used to
compute the weighted average in the`,name:"cross_attentions"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/prophetnet/modeling_prophetnet.py#L418"}}),He=new $({props:{name:"class transformers.models.prophetnet.modeling_prophetnet.ProphetNetDecoderLMOutput",anchor:"transformers.models.prophetnet.modeling_prophetnet.ProphetNetDecoderLMOutput",parameters:[{name:"loss",val:": Optional = None"},{name:"logits",val:": FloatTensor = None"},{name:"logits_ngram",val:": Optional = None"},{name:"past_key_values",val:": Optional = None"},{name:"hidden_states",val:": Optional = None"},{name:"hidden_states_ngram",val:": Optional = None"},{name:"attentions",val:": Optional = None"},{name:"ngram_attentions",val:": Optional = None"},{name:"cross_attentions",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.models.prophetnet.modeling_prophetnet.ProphetNetDecoderLMOutput.loss",description:`<strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) &#x2014;
Language modeling loss.`,name:"loss"},{anchor:"transformers.models.prophetnet.modeling_prophetnet.ProphetNetDecoderLMOutput.logits",description:`<strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, decoder_sequence_length, config.vocab_size)</code>) &#x2014;
Prediction scores of the main stream language modeling head (scores for each vocabulary token before
SoftMax).`,name:"logits"},{anchor:"transformers.models.prophetnet.modeling_prophetnet.ProphetNetDecoderLMOutput.logits_ngram",description:`<strong>logits_ngram</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, ngram * decoder_sequence_length, config.vocab_size)</code>) &#x2014;
Prediction scores of the predict stream language modeling head (scores for each vocabulary token before
SoftMax).`,name:"logits_ngram"},{anchor:"transformers.models.prophetnet.modeling_prophetnet.ProphetNetDecoderLMOutput.past_key_values",description:`<strong>past_key_values</strong> (<code>List[torch.FloatTensor]</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) &#x2014;
List of <code>torch.FloatTensor</code> of length <code>config.n_layers</code>, with each tensor of shape <code>(2, batch_size, num_attn_heads, decoder_sequence_length, embed_size_per_head)</code>).</p>
<p>Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see <code>past_key_values</code> input) to speed up sequential decoding.`,name:"past_key_values"},{anchor:"transformers.models.prophetnet.modeling_prophetnet.ProphetNetDecoderLMOutput.hidden_states",description:`<strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) &#x2014;
Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, decoder_sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of main stream of the decoder at the output of each layer plus the initial embedding outputs.`,name:"hidden_states"},{anchor:"transformers.models.prophetnet.modeling_prophetnet.ProphetNetDecoderLMOutput.ngram_hidden_states",description:`<strong>ngram_hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) &#x2014;
Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, ngram * decoder_sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the predict stream of the decoder at the output of each layer plus the initial embedding
outputs.`,name:"ngram_hidden_states"},{anchor:"transformers.models.prophetnet.modeling_prophetnet.ProphetNetDecoderLMOutput.attentions",description:`<strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) &#x2014;
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_attn_heads, decoder_sequence_length, decoder_sequence_length)</code>.</p>
<p>Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.`,name:"attentions"},{anchor:"transformers.models.prophetnet.modeling_prophetnet.ProphetNetDecoderLMOutput.ngram_attentions",description:`<strong>ngram_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) &#x2014;
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_attn_heads, decoder_sequence_length, decoder_sequence_length)</code>.</p>
<p>Attentions weights of the predict stream of the decoder, after the attention softmax, used to compute the
weighted average in the`,name:"ngram_attentions"},{anchor:"transformers.models.prophetnet.modeling_prophetnet.ProphetNetDecoderLMOutput.cross_attentions",description:`<strong>cross_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) &#x2014;
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_attn_heads, encoder_sequence_length, decoder_sequence_length)</code>.</p>
<p>Attentions weights of the cross-attention layer of the decoder, after the attention softmax, used to
compute the weighted average in the`,name:"cross_attentions"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/prophetnet/modeling_prophetnet.py#L478"}}),Se=new S({props:{title:"ProphetNetModel",local:"transformers.ProphetNetModel",headingTag:"h2"}}),Ve=new $({props:{name:"class transformers.ProphetNetModel",anchor:"transformers.ProphetNetModel",parameters:[{name:"config",val:": ProphetNetConfig"}],parametersDescription:[{anchor:"transformers.ProphetNetModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/prophetnet#transformers.ProphetNetConfig">ProphetNetConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/prophetnet/modeling_prophetnet.py#L1721"}}),Be=new $({props:{name:"forward",anchor:"transformers.ProphetNetModel.forward",parameters:[{name:"input_ids",val:": Optional = None"},{name:"attention_mask",val:": Optional = None"},{name:"decoder_input_ids",val:": Optional = None"},{name:"decoder_attention_mask",val:": Optional = None"},{name:"head_mask",val:": Optional = None"},{name:"decoder_head_mask",val:": Optional = None"},{name:"cross_attn_head_mask",val:": Optional = None"},{name:"encoder_outputs",val:": Optional = None"},{name:"past_key_values",val:": Optional = None"},{name:"inputs_embeds",val:": Optional = None"},{name:"decoder_inputs_embeds",val:": Optional = None"},{name:"use_cache",val:": Optional = None"},{name:"output_attentions",val:": Optional = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.ProphetNetModel.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.ProphetNetModel.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.ProphetNetModel.forward.decoder_input_ids",description:`<strong>decoder_input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, target_sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of decoder input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#decoder-input-ids">What are decoder input IDs?</a></p>
<p>ProphetNet uses the <code>eos_token_id</code> as the starting token for <code>decoder_input_ids</code> generation. If
<code>past_key_values</code> is used, optionally only the last <code>decoder_input_ids</code> have to be input (see
<code>past_key_values</code>).`,name:"decoder_input_ids"},{anchor:"transformers.ProphetNetModel.forward.decoder_attention_mask",description:`<strong>decoder_attention_mask</strong> (<code>torch.BoolTensor</code> of shape <code>(batch_size, target_sequence_length)</code>, <em>optional</em>) &#x2014;
Default behavior: generate a tensor that ignores pad tokens in <code>decoder_input_ids</code>. Causal mask will also
be used by default.`,name:"decoder_attention_mask"},{anchor:"transformers.ProphetNetModel.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.Tensor</code> of shape <code>(encoder_layers, encoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.ProphetNetModel.forward.decoder_head_mask",description:`<strong>decoder_head_mask</strong> (<code>torch.Tensor</code> of shape <code>(decoder_layers, decoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"decoder_head_mask"},{anchor:"transformers.ProphetNetModel.forward.cross_attn_head_mask",description:`<strong>cross_attn_head_mask</strong> (<code>torch.Tensor</code> of shape <code>(decoder_layers, decoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the cross-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"cross_attn_head_mask"},{anchor:"transformers.ProphetNetModel.forward.encoder_outputs",description:`<strong>encoder_outputs</strong> (<code>tuple(tuple(torch.FloatTensor)</code>, <em>optional</em>) &#x2014;
Tuple consists of (<code>last_hidden_state</code>, <em>optional</em>: <code>hidden_states</code>, <em>optional</em>: <code>attentions</code>)
<code>last_hidden_state</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) is a sequence of
hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.`,name:"encoder_outputs"},{anchor:"transformers.ProphetNetModel.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code> of length <code>config.n_layers</code> with each tuple having 4 tensors of shape <code>(batch_size, num_heads, sequence_length - 1, embed_size_per_head)</code>) &#x2014;
Contains precomputed key and value hidden-states of the attention blocks. Can be used to speed up decoding.</p>
<p>If <code>past_key_values</code> are used, the user can optionally input only the last <code>decoder_input_ids</code> (those that
don&#x2019;t have their past key value states given to this model) of shape <code>(batch_size, 1)</code> instead of all
<code>decoder_input_ids</code> of shape <code>(batch_size, sequence_length)</code>.`,name:"past_key_values"},{anchor:"transformers.ProphetNetModel.forward.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
If set to <code>True</code>, <code>past_key_values</code> key value states are returned and can be used to speed up decoding (see
<code>past_key_values</code>).`,name:"use_cache"},{anchor:"transformers.ProphetNetModel.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.ProphetNetModel.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.ProphetNetModel.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/prophetnet/modeling_prophetnet.py#L1764",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/main/en/model_doc/prophetnet#transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqModelOutput"
>transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqModelOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<code>ProphenetConfig</code>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, decoder_sequence_length, hidden_size)</code>) — Sequence of main stream hidden-states at the output of the last layer of the decoder of the model.</p>
<p>If <code>past_key_values</code> is used only the last hidden-state of the sequences of shape <code>(batch_size, 1, hidden_size)</code> is output.</p>
</li>
<li>
<p><strong>last_hidden_state_ngram</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size,ngram * decoder_sequence_length, config.vocab_size)</code>, <em>optional</em>) — Sequence of predict stream hidden-states at the output of the last layer of the decoder of the model.</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>List[torch.FloatTensor]</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) — List of <code>torch.FloatTensor</code> of length <code>config.n_layers</code>, with each tensor of shape <code>(2, batch_size, num_attn_heads, decoder_sequence_length, embed_size_per_head)</code>).</p>
<p>Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see <code>past_key_values</code> input) to speed up sequential decoding.</p>
</li>
<li>
<p><strong>decoder_hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, decoder_sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of main stream of the decoder at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>decoder_ngram_hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, ngram * decoder_sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the predict stream of the decoder at the output of each layer plus the initial embedding
outputs.</p>
</li>
<li>
<p><strong>decoder_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_attn_heads, decoder_sequence_length, decoder_sequence_length)</code>.</p>
<p>Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.</p>
</li>
<li>
<p><strong>decoder_ngram_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_attn_heads, decoder_sequence_length, decoder_sequence_length)</code>.</p>
<p>Attentions weights of the predict stream of the decoder, after the attention softmax, used to compute the
weighted average in the</p>
</li>
<li>
<p><strong>cross_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_attn_heads, encoder_sequence_length, decoder_sequence_length)</code>.</p>
<p>Attentions weights of the cross-attention layer of the decoder, after the attention softmax, used to
compute the weighted average in the</p>
</li>
<li>
<p><strong>encoder_last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, encoder_sequence_length, hidden_size)</code>, <em>optional</em>) — Sequence of hidden-states at the output of the last layer of the encoder of the model.</p>
</li>
<li>
<p><strong>encoder_hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, encoder_sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>encoder_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_attn_heads, encoder_sequence_length, encoder_sequence_length)</code>.</p>
<p>Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.</p>
</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/main/en/model_doc/prophetnet#transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqModelOutput"
>transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),re=new At({props:{$$slots:{default:[qs]},$$scope:{ctx:w}}}),ae=new Et({props:{anchor:"transformers.ProphetNetModel.forward.example",$$slots:{default:[Cs]},$$scope:{ctx:w}}}),Re=new S({props:{title:"ProphetNetEncoder",local:"transformers.ProphetNetEncoder",headingTag:"h2"}}),Ee=new $({props:{name:"class transformers.ProphetNetEncoder",anchor:"transformers.ProphetNetEncoder",parameters:[{name:"config",val:": ProphetNetConfig"},{name:"word_embeddings",val:": Embedding = None"}],parametersDescription:[{anchor:"transformers.ProphetNetEncoder.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/prophetnet#transformers.ProphetNetConfig">ProphetNetConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/prophetnet/modeling_prophetnet.py#L1219"}}),De=new $({props:{name:"forward",anchor:"transformers.ProphetNetEncoder.forward",parameters:[{name:"input_ids",val:": Optional = None"},{name:"attention_mask",val:": Optional = None"},{name:"head_mask",val:": Optional = None"},{name:"inputs_embeds",val:": Optional = None"},{name:"output_attentions",val:": Optional = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.ProphetNetEncoder.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.ProphetNetEncoder.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.ProphetNetEncoder.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.Tensor</code> of shape <code>(encoder_layers, encoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.ProphetNetEncoder.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.ProphetNetEncoder.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.ProphetNetEncoder.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/prophetnet/modeling_prophetnet.py#L1253",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutput"
>transformers.modeling_outputs.BaseModelOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<code>ProphenetConfig</code>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) — Sequence of hidden-states at the output of the last layer of the model.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutput"
>transformers.modeling_outputs.BaseModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),de=new At({props:{$$slots:{default:[Fs]},$$scope:{ctx:w}}}),ie=new Et({props:{anchor:"transformers.ProphetNetEncoder.forward.example",$$slots:{default:[js]},$$scope:{ctx:w}}}),Xe=new S({props:{title:"ProphetNetDecoder",local:"transformers.ProphetNetDecoder",headingTag:"h2"}}),Ae=new $({props:{name:"class transformers.ProphetNetDecoder",anchor:"transformers.ProphetNetDecoder",parameters:[{name:"config",val:": ProphetNetConfig"},{name:"word_embeddings",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.ProphetNetDecoder.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/prophetnet#transformers.ProphetNetConfig">ProphetNetConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/prophetnet/modeling_prophetnet.py#L1353"}}),Qe=new $({props:{name:"forward",anchor:"transformers.ProphetNetDecoder.forward",parameters:[{name:"input_ids",val:": Optional = None"},{name:"attention_mask",val:": Optional = None"},{name:"encoder_hidden_states",val:": Optional = None"},{name:"encoder_attention_mask",val:": Optional = None"},{name:"head_mask",val:": Optional = None"},{name:"cross_attn_head_mask",val:": Optional = None"},{name:"past_key_values",val:": Optional = None"},{name:"inputs_embeds",val:": Optional = None"},{name:"use_cache",val:": Optional = None"},{name:"output_attentions",val:": Optional = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.ProphetNetDecoder.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.ProphetNetDecoder.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.ProphetNetDecoder.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.Tensor</code> of shape <code>(encoder_layers, encoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.ProphetNetDecoder.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.ProphetNetDecoder.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.ProphetNetDecoder.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.ProphetNetDecoder.forward.encoder_hidden_states",description:`<strong>encoder_hidden_states</strong>  (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.`,name:"encoder_hidden_states"},{anchor:"transformers.ProphetNetDecoder.forward.encoder_attention_mask",description:`<strong>encoder_attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in <code>[0, 1]</code>:`,name:"encoder_attention_mask"},{anchor:"transformers.ProphetNetDecoder.forward.cross_attn_head_mask",description:`<strong>cross_attn_head_mask</strong> (<code>torch.Tensor</code> of shape <code>(decoder_layers, decoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the cross-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"cross_attn_head_mask"},{anchor:"transformers.ProphetNetDecoder.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code> of length <code>config.n_layers</code> with each tuple having 4 tensors of shape <code>(batch_size, num_heads, sequence_length - 1, embed_size_per_head)</code>) &#x2014;
Contains precomputed key and value hidden-states of the attention blocks. Can be used to speed up decoding.</p>
<p>If <code>past_key_values</code> are used, the user can optionally input only the last <code>decoder_input_ids</code> (those that
don&#x2019;t have their past key value states given to this model) of shape <code>(batch_size, 1)</code> instead of all
<code>decoder_input_ids</code> of shape <code>(batch_size, sequence_length)</code>.`,name:"past_key_values"},{anchor:"transformers.ProphetNetDecoder.forward.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
If set to <code>True</code>, <code>past_key_values</code> key value states are returned and can be used to speed up decoding (see
<code>past_key_values</code>).</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>`,name:"use_cache"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/prophetnet/modeling_prophetnet.py#L1394",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/main/en/model_doc/prophetnet#transformers.models.prophetnet.modeling_prophetnet.ProphetNetDecoderModelOutput"
>transformers.models.prophetnet.modeling_prophetnet.ProphetNetDecoderModelOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<code>ProphenetConfig</code>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, decoder_sequence_length, hidden_size)</code>) — Sequence of main stream hidden-states at the output of the last layer of the decoder of the model.</p>
<p>If <code>past_key_values</code> is used only the last hidden-state of the sequences of shape <code>(batch_size, 1, hidden_size)</code> is output.</p>
</li>
<li>
<p><strong>last_hidden_state_ngram</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, ngram * decoder_sequence_length, config.vocab_size)</code>) — Sequence of predict stream hidden-states at the output of the last layer of the decoder of the model.</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>List[torch.FloatTensor]</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) — List of <code>torch.FloatTensor</code> of length <code>config.n_layers</code>, with each tensor of shape <code>(2, batch_size, num_attn_heads, decoder_sequence_length, embed_size_per_head)</code>).</p>
<p>Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see <code>past_key_values</code> input) to speed up sequential decoding.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, decoder_sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of main stream of the decoder at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>ngram_hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, ngram * decoder_sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the predict stream of the decoder at the output of each layer plus the initial embedding
outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_attn_heads, decoder_sequence_length, decoder_sequence_length)</code>.</p>
<p>Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.</p>
</li>
<li>
<p><strong>ngram_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_attn_heads, decoder_sequence_length, decoder_sequence_length)</code>.</p>
<p>Attentions weights of the predict stream of the decoder, after the attention softmax, used to compute the
weighted average in the</p>
</li>
<li>
<p><strong>cross_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_attn_heads, encoder_sequence_length, decoder_sequence_length)</code>.</p>
<p>Attentions weights of the cross-attention layer of the decoder, after the attention softmax, used to
compute the weighted average in the</p>
</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/main/en/model_doc/prophetnet#transformers.models.prophetnet.modeling_prophetnet.ProphetNetDecoderModelOutput"
>transformers.models.prophetnet.modeling_prophetnet.ProphetNetDecoderModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),ce=new At({props:{$$slots:{default:[Js]},$$scope:{ctx:w}}}),le=new Et({props:{anchor:"transformers.ProphetNetDecoder.forward.example",$$slots:{default:[Us]},$$scope:{ctx:w}}}),Ye=new S({props:{title:"ProphetNetForConditionalGeneration",local:"transformers.ProphetNetForConditionalGeneration",headingTag:"h2"}}),Ke=new $({props:{name:"class transformers.ProphetNetForConditionalGeneration",anchor:"transformers.ProphetNetForConditionalGeneration",parameters:[{name:"config",val:": ProphetNetConfig"}],parametersDescription:[{anchor:"transformers.ProphetNetForConditionalGeneration.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/prophetnet#transformers.ProphetNetConfig">ProphetNetConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/prophetnet/modeling_prophetnet.py#L1855"}}),et=new $({props:{name:"forward",anchor:"transformers.ProphetNetForConditionalGeneration.forward",parameters:[{name:"input_ids",val:": Optional = None"},{name:"attention_mask",val:": Optional = None"},{name:"decoder_input_ids",val:": Optional = None"},{name:"decoder_attention_mask",val:": Optional = None"},{name:"head_mask",val:": Optional = None"},{name:"decoder_head_mask",val:": Optional = None"},{name:"cross_attn_head_mask",val:": Optional = None"},{name:"encoder_outputs",val:": Optional = None"},{name:"past_key_values",val:": Optional = None"},{name:"inputs_embeds",val:": Optional = None"},{name:"decoder_inputs_embeds",val:": Optional = None"},{name:"labels",val:": Optional = None"},{name:"use_cache",val:": Optional = None"},{name:"output_attentions",val:": Optional = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.ProphetNetForConditionalGeneration.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.ProphetNetForConditionalGeneration.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.ProphetNetForConditionalGeneration.forward.decoder_input_ids",description:`<strong>decoder_input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, target_sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of decoder input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#decoder-input-ids">What are decoder input IDs?</a></p>
<p>ProphetNet uses the <code>eos_token_id</code> as the starting token for <code>decoder_input_ids</code> generation. If
<code>past_key_values</code> is used, optionally only the last <code>decoder_input_ids</code> have to be input (see
<code>past_key_values</code>).`,name:"decoder_input_ids"},{anchor:"transformers.ProphetNetForConditionalGeneration.forward.decoder_attention_mask",description:`<strong>decoder_attention_mask</strong> (<code>torch.BoolTensor</code> of shape <code>(batch_size, target_sequence_length)</code>, <em>optional</em>) &#x2014;
Default behavior: generate a tensor that ignores pad tokens in <code>decoder_input_ids</code>. Causal mask will also
be used by default.`,name:"decoder_attention_mask"},{anchor:"transformers.ProphetNetForConditionalGeneration.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.Tensor</code> of shape <code>(encoder_layers, encoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.ProphetNetForConditionalGeneration.forward.decoder_head_mask",description:`<strong>decoder_head_mask</strong> (<code>torch.Tensor</code> of shape <code>(decoder_layers, decoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"decoder_head_mask"},{anchor:"transformers.ProphetNetForConditionalGeneration.forward.cross_attn_head_mask",description:`<strong>cross_attn_head_mask</strong> (<code>torch.Tensor</code> of shape <code>(decoder_layers, decoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the cross-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"cross_attn_head_mask"},{anchor:"transformers.ProphetNetForConditionalGeneration.forward.encoder_outputs",description:`<strong>encoder_outputs</strong> (<code>tuple(tuple(torch.FloatTensor)</code>, <em>optional</em>) &#x2014;
Tuple consists of (<code>last_hidden_state</code>, <em>optional</em>: <code>hidden_states</code>, <em>optional</em>: <code>attentions</code>)
<code>last_hidden_state</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) is a sequence of
hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.`,name:"encoder_outputs"},{anchor:"transformers.ProphetNetForConditionalGeneration.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code> of length <code>config.n_layers</code> with each tuple having 4 tensors of shape <code>(batch_size, num_heads, sequence_length - 1, embed_size_per_head)</code>) &#x2014;
Contains precomputed key and value hidden-states of the attention blocks. Can be used to speed up decoding.</p>
<p>If <code>past_key_values</code> are used, the user can optionally input only the last <code>decoder_input_ids</code> (those that
don&#x2019;t have their past key value states given to this model) of shape <code>(batch_size, 1)</code> instead of all
<code>decoder_input_ids</code> of shape <code>(batch_size, sequence_length)</code>.`,name:"past_key_values"},{anchor:"transformers.ProphetNetForConditionalGeneration.forward.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
If set to <code>True</code>, <code>past_key_values</code> key value states are returned and can be used to speed up decoding (see
<code>past_key_values</code>).`,name:"use_cache"},{anchor:"transformers.ProphetNetForConditionalGeneration.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.ProphetNetForConditionalGeneration.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.ProphetNetForConditionalGeneration.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.ProphetNetForConditionalGeneration.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for computing the sequence classification/regression loss. Indices should be in <code>[-100, 0, ..., config.vocab_size - 1]</code>. All labels set to <code>-100</code> are ignored (masked), the loss is only computed for
labels in <code>[0, ..., config.vocab_size]</code>`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/prophetnet/modeling_prophetnet.py#L1886",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/main/en/model_doc/prophetnet#transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqLMOutput"
>transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqLMOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<code>ProphenetConfig</code>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) — Language modeling loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, decoder_sequence_length, config.vocab_size)</code>) — Prediction scores of the main stream language modeling head (scores for each vocabulary token before
SoftMax).</p>
</li>
<li>
<p><strong>logits_ngram</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, ngram * decoder_sequence_length, config.vocab_size)</code>) — Prediction scores of the predict stream language modeling head (scores for each vocabulary token before
SoftMax).</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>List[torch.FloatTensor]</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) — List of <code>torch.FloatTensor</code> of length <code>config.n_layers</code>, with each tensor of shape <code>(2, batch_size, num_attn_heads, decoder_sequence_length, embed_size_per_head)</code>).</p>
<p>Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see <code>past_key_values</code> input) to speed up sequential decoding.</p>
</li>
<li>
<p><strong>decoder_hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, decoder_sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of main stream of the decoder at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>decoder_ngram_hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, ngram * decoder_sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the predict stream of the decoder at the output of each layer plus the initial embedding
outputs.</p>
</li>
<li>
<p><strong>decoder_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_attn_heads, decoder_sequence_length, decoder_sequence_length)</code>.</p>
<p>Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.</p>
</li>
<li>
<p><strong>decoder_ngram_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_attn_heads, decoder_sequence_length, decoder_sequence_length)</code>.</p>
<p>Attentions weights of the predict stream of the decoder, after the attention softmax, used to compute the
weighted average in the self-attention heads.</p>
</li>
<li>
<p><strong>cross_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_attn_heads, encoder_sequence_length, decoder_sequence_length)</code>.</p>
<p>Attentions weights of the cross-attention layer of the decoder, after the attention softmax, used to
compute the weighted average in the</p>
</li>
<li>
<p><strong>encoder_last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, encoder_sequence_length, hidden_size)</code>, <em>optional</em>) — Sequence of hidden-states at the output of the last layer of the encoder of the model.</p>
</li>
<li>
<p><strong>encoder_hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, encoder_sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>encoder_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_attn_heads, encoder_sequence_length, encoder_sequence_length)</code>. Attentions weights of the encoder, after the attention
softmax, used to compute the weighted average in the self-attention heads.</p>
</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/main/en/model_doc/prophetnet#transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqLMOutput"
>transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqLMOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),pe=new At({props:{$$slots:{default:[Is]},$$scope:{ctx:w}}}),he=new Et({props:{anchor:"transformers.ProphetNetForConditionalGeneration.forward.example",$$slots:{default:[Ls]},$$scope:{ctx:w}}}),tt=new S({props:{title:"ProphetNetForCausalLM",local:"transformers.ProphetNetForCausalLM",headingTag:"h2"}}),ot=new $({props:{name:"class transformers.ProphetNetForCausalLM",anchor:"transformers.ProphetNetForCausalLM",parameters:[{name:"config",val:": ProphetNetConfig"}],parametersDescription:[{anchor:"transformers.ProphetNetForCausalLM.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/prophetnet#transformers.ProphetNetConfig">ProphetNetConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/prophetnet/modeling_prophetnet.py#L2071"}}),nt=new $({props:{name:"forward",anchor:"transformers.ProphetNetForCausalLM.forward",parameters:[{name:"input_ids",val:": Optional = None"},{name:"attention_mask",val:": Optional = None"},{name:"encoder_hidden_states",val:": Optional = None"},{name:"encoder_attention_mask",val:": Optional = None"},{name:"head_mask",val:": Optional = None"},{name:"cross_attn_head_mask",val:": Optional = None"},{name:"past_key_values",val:": Optional = None"},{name:"inputs_embeds",val:": Optional = None"},{name:"labels",val:": Optional = None"},{name:"use_cache",val:": Optional = None"},{name:"output_attentions",val:": Optional = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.ProphetNetForCausalLM.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.ProphetNetForCausalLM.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.ProphetNetForCausalLM.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.Tensor</code> of shape <code>(encoder_layers, encoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.ProphetNetForCausalLM.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.ProphetNetForCausalLM.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.ProphetNetForCausalLM.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.ProphetNetForCausalLM.forward.encoder_hidden_states",description:`<strong>encoder_hidden_states</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.`,name:"encoder_hidden_states"},{anchor:"transformers.ProphetNetForCausalLM.forward.encoder_attention_mask",description:`<strong>encoder_attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in <code>[0, 1]</code>:`,name:"encoder_attention_mask"},{anchor:"transformers.ProphetNetForCausalLM.forward.cross_attn_head_mask",description:`<strong>cross_attn_head_mask</strong> (<code>torch.Tensor</code> of shape <code>(decoder_layers, decoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the cross-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"cross_attn_head_mask"},{anchor:"transformers.ProphetNetForCausalLM.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code> of length <code>config.n_layers</code> with each tuple having 4 tensors of shape <code>(batch_size, num_heads, sequence_length - 1, embed_size_per_head)</code>) &#x2014;
Contains precomputed key and value hidden-states of the attention blocks. Can be used to speed up decoding.</p>
<p>If <code>past_key_values</code> are used, the user can optionally input only the last <code>decoder_input_ids</code> (those that
don&#x2019;t have their past key value states given to this model) of shape <code>(batch_size, 1)</code> instead of all
<code>decoder_input_ids</code> of shape <code>(batch_size, sequence_length)</code>.`,name:"past_key_values"},{anchor:"transformers.ProphetNetForCausalLM.forward.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
If set to <code>True</code>, <code>past_key_values</code> key value states are returned and can be used to speed up decoding (see
<code>past_key_values</code>).</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>`,name:"use_cache"},{anchor:"transformers.ProphetNetForCausalLM.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
<code>[-100, 0, ..., config.vocab_size]</code> (see <code>input_ids</code> docstring) Tokens with indices set to <code>-100</code> are
ignored (masked), the loss is only computed for the tokens with labels n <code>[0, ..., config.vocab_size]</code>`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/prophetnet/modeling_prophetnet.py#L2121",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/main/en/model_doc/prophetnet#transformers.models.prophetnet.modeling_prophetnet.ProphetNetDecoderLMOutput"
>transformers.models.prophetnet.modeling_prophetnet.ProphetNetDecoderLMOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<code>ProphenetConfig</code>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) — Language modeling loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, decoder_sequence_length, config.vocab_size)</code>) — Prediction scores of the main stream language modeling head (scores for each vocabulary token before
SoftMax).</p>
</li>
<li>
<p><strong>logits_ngram</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, ngram * decoder_sequence_length, config.vocab_size)</code>) — Prediction scores of the predict stream language modeling head (scores for each vocabulary token before
SoftMax).</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>List[torch.FloatTensor]</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) — List of <code>torch.FloatTensor</code> of length <code>config.n_layers</code>, with each tensor of shape <code>(2, batch_size, num_attn_heads, decoder_sequence_length, embed_size_per_head)</code>).</p>
<p>Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see <code>past_key_values</code> input) to speed up sequential decoding.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, decoder_sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of main stream of the decoder at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>ngram_hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, ngram * decoder_sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the predict stream of the decoder at the output of each layer plus the initial embedding
outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_attn_heads, decoder_sequence_length, decoder_sequence_length)</code>.</p>
<p>Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.</p>
</li>
<li>
<p><strong>ngram_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_attn_heads, decoder_sequence_length, decoder_sequence_length)</code>.</p>
<p>Attentions weights of the predict stream of the decoder, after the attention softmax, used to compute the
weighted average in the</p>
</li>
<li>
<p><strong>cross_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_attn_heads, encoder_sequence_length, decoder_sequence_length)</code>.</p>
<p>Attentions weights of the cross-attention layer of the decoder, after the attention softmax, used to
compute the weighted average in the</p>
</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/main/en/model_doc/prophetnet#transformers.models.prophetnet.modeling_prophetnet.ProphetNetDecoderLMOutput"
>transformers.models.prophetnet.modeling_prophetnet.ProphetNetDecoderLMOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),me=new At({props:{$$slots:{default:[Os]},$$scope:{ctx:w}}}),ue=new Et({props:{anchor:"transformers.ProphetNetForCausalLM.forward.example",$$slots:{default:[Gs]},$$scope:{ctx:w}}}),st=new Ps({props:{source:"https://github.com/huggingface/transformers/blob/main/docs/source/en/model_doc/prophetnet.md"}}),{c(){o=i("meta"),y=n(),p=i("p"),h=n(),u(k.$$.fragment),a=n(),v=i("div"),v.innerHTML=jn,Qt=n(),u(ye.$$.fragment),Yt=n(),ke=i("p"),ke.innerHTML=Jn,Kt=n(),ve=i("p"),ve.textContent=Un,eo=n(),we=i("p"),we.textContent=In,to=n(),Me=i("p"),Me.innerHTML=Ln,oo=n(),Ne=i("p"),Ne.innerHTML=On,no=n(),u($e.$$.fragment),so=n(),ze=i("ul"),ze.innerHTML=Gn,ro=n(),u(Pe.$$.fragment),ao=n(),xe=i("ul"),xe.innerHTML=Zn,io=n(),u(qe.$$.fragment),co=n(),V=i("div"),u(Ce.$$.fragment),qo=n(),pt=i("p"),pt.innerHTML=Wn,Co=n(),ht=i("p"),ht.innerHTML=Hn,lo=n(),u(Fe.$$.fragment),po=n(),z=i("div"),u(je.$$.fragment),Fo=n(),mt=i("p"),mt.textContent=Sn,jo=n(),ut=i("p"),ut.innerHTML=Vn,Jo=n(),E=i("div"),u(Je.$$.fragment),Uo=n(),ft=i("p"),ft.textContent=Bn,Io=n(),_t=i("ul"),_t.innerHTML=Rn,Lo=n(),oe=i("div"),u(Ue.$$.fragment),Oo=n(),gt=i("p"),gt.textContent=En,Go=n(),U=i("div"),u(Ie.$$.fragment),Zo=n(),Tt=i("p"),Tt.textContent=Dn,Wo=n(),u(ne.$$.fragment),Ho=n(),bt=i("p"),bt.innerHTML=Xn,So=n(),se=i("div"),u(Le.$$.fragment),Vo=n(),yt=i("p"),yt.innerHTML=An,ho=n(),u(Oe.$$.fragment),mo=n(),A=i("div"),u(Ge.$$.fragment),Bo=n(),kt=i("p"),kt.textContent=Qn,uo=n(),Q=i("div"),u(Ze.$$.fragment),Ro=n(),vt=i("p"),vt.textContent=Yn,fo=n(),Y=i("div"),u(We.$$.fragment),Eo=n(),wt=i("p"),wt.textContent=Kn,_o=n(),K=i("div"),u(He.$$.fragment),Do=n(),Mt=i("p"),Mt.textContent=es,go=n(),u(Se.$$.fragment),To=n(),C=i("div"),u(Ve.$$.fragment),Xo=n(),Nt=i("p"),Nt.innerHTML=ts,Ao=n(),$t=i("p"),$t.innerHTML=os,Qo=n(),zt=i("p"),zt.innerHTML=ns,Yo=n(),I=i("div"),u(Be.$$.fragment),Ko=n(),Pt=i("p"),Pt.innerHTML=ss,en=n(),u(re.$$.fragment),tn=n(),u(ae.$$.fragment),bo=n(),u(Re.$$.fragment),yo=n(),P=i("div"),u(Ee.$$.fragment),on=n(),xt=i("p"),xt.innerHTML=rs,nn=n(),qt=i("p"),qt.innerHTML=as,sn=n(),Ct=i("p"),Ct.innerHTML=ds,rn=n(),Ft=i("p"),Ft.innerHTML=is,an=n(),L=i("div"),u(De.$$.fragment),dn=n(),jt=i("p"),jt.innerHTML=cs,cn=n(),u(de.$$.fragment),ln=n(),u(ie.$$.fragment),ko=n(),u(Xe.$$.fragment),vo=n(),x=i("div"),u(Ae.$$.fragment),pn=n(),Jt=i("p"),Jt.innerHTML=ls,hn=n(),Ut=i("p"),Ut.innerHTML=ps,mn=n(),It=i("p"),It.innerHTML=hs,un=n(),Lt=i("p"),Lt.innerHTML=ms,fn=n(),O=i("div"),u(Qe.$$.fragment),_n=n(),Ot=i("p"),Ot.innerHTML=us,gn=n(),u(ce.$$.fragment),Tn=n(),u(le.$$.fragment),wo=n(),u(Ye.$$.fragment),Mo=n(),F=i("div"),u(Ke.$$.fragment),bn=n(),Gt=i("p"),Gt.innerHTML=fs,yn=n(),Zt=i("p"),Zt.innerHTML=_s,kn=n(),Wt=i("p"),Wt.innerHTML=gs,vn=n(),G=i("div"),u(et.$$.fragment),wn=n(),Ht=i("p"),Ht.innerHTML=Ts,Mn=n(),u(pe.$$.fragment),Nn=n(),u(he.$$.fragment),No=n(),u(tt.$$.fragment),$o=n(),j=i("div"),u(ot.$$.fragment),$n=n(),St=i("p"),St.innerHTML=bs,zn=n(),Vt=i("p"),Vt.innerHTML=ys,Pn=n(),Bt=i("p"),Bt.innerHTML=ks,xn=n(),Z=i("div"),u(nt.$$.fragment),qn=n(),Rt=i("p"),Rt.innerHTML=vs,Cn=n(),u(me.$$.fragment),Fn=n(),u(ue.$$.fragment),zo=n(),u(st.$$.fragment),Po=n(),Xt=i("p"),this.h()},l(e){const d=zs("svelte-u9bgzb",document.head);o=c(d,"META",{name:!0,content:!0}),d.forEach(r),y=s(e),p=c(e,"P",{}),N(p).forEach(r),h=s(e),f(k.$$.fragment,e),a=s(e),v=c(e,"DIV",{class:!0,"data-svelte-h":!0}),m(v)!=="svelte-bu151u"&&(v.innerHTML=jn),Qt=s(e),f(ye.$$.fragment,e),Yt=s(e),ke=c(e,"P",{"data-svelte-h":!0}),m(ke)!=="svelte-1bjka46"&&(ke.innerHTML=Jn),Kt=s(e),ve=c(e,"P",{"data-svelte-h":!0}),m(ve)!=="svelte-s2k052"&&(ve.textContent=Un),eo=s(e),we=c(e,"P",{"data-svelte-h":!0}),m(we)!=="svelte-vfdo9a"&&(we.textContent=In),to=s(e),Me=c(e,"P",{"data-svelte-h":!0}),m(Me)!=="svelte-1jvtdli"&&(Me.innerHTML=Ln),oo=s(e),Ne=c(e,"P",{"data-svelte-h":!0}),m(Ne)!=="svelte-mvxxnf"&&(Ne.innerHTML=On),no=s(e),f($e.$$.fragment,e),so=s(e),ze=c(e,"UL",{"data-svelte-h":!0}),m(ze)!=="svelte-12f658v"&&(ze.innerHTML=Gn),ro=s(e),f(Pe.$$.fragment,e),ao=s(e),xe=c(e,"UL",{"data-svelte-h":!0}),m(xe)!=="svelte-jwyjs9"&&(xe.innerHTML=Zn),io=s(e),f(qe.$$.fragment,e),co=s(e),V=c(e,"DIV",{class:!0});var ee=N(V);f(Ce.$$.fragment,ee),qo=s(ee),pt=c(ee,"P",{"data-svelte-h":!0}),m(pt)!=="svelte-byjef6"&&(pt.innerHTML=Wn),Co=s(ee),ht=c(ee,"P",{"data-svelte-h":!0}),m(ht)!=="svelte-o55m63"&&(ht.innerHTML=Hn),ee.forEach(r),lo=s(e),f(Fe.$$.fragment,e),po=s(e),z=c(e,"DIV",{class:!0});var q=N(z);f(je.$$.fragment,q),Fo=s(q),mt=c(q,"P",{"data-svelte-h":!0}),m(mt)!=="svelte-rzl6zy"&&(mt.textContent=Sn),jo=s(q),ut=c(q,"P",{"data-svelte-h":!0}),m(ut)!=="svelte-1c3t5ty"&&(ut.innerHTML=Vn),Jo=s(q),E=c(q,"DIV",{class:!0});var te=N(E);f(Je.$$.fragment,te),Uo=s(te),ft=c(te,"P",{"data-svelte-h":!0}),m(ft)!=="svelte-t7qurq"&&(ft.textContent=Bn),Io=s(te),_t=c(te,"UL",{"data-svelte-h":!0}),m(_t)!=="svelte-xi6653"&&(_t.innerHTML=Rn),te.forEach(r),Lo=s(q),oe=c(q,"DIV",{class:!0});var rt=N(oe);f(Ue.$$.fragment,rt),Oo=s(rt),gt=c(rt,"P",{"data-svelte-h":!0}),m(gt)!=="svelte-b3k2yi"&&(gt.textContent=En),rt.forEach(r),Go=s(q),U=c(q,"DIV",{class:!0});var B=N(U);f(Ie.$$.fragment,B),Zo=s(B),Tt=c(B,"P",{"data-svelte-h":!0}),m(Tt)!=="svelte-1szr3b2"&&(Tt.textContent=Dn),Wo=s(B),f(ne.$$.fragment,B),Ho=s(B),bt=c(B,"P",{"data-svelte-h":!0}),m(bt)!=="svelte-owoxgn"&&(bt.innerHTML=Xn),B.forEach(r),So=s(q),se=c(q,"DIV",{class:!0});var at=N(se);f(Le.$$.fragment,at),Vo=s(at),yt=c(at,"P",{"data-svelte-h":!0}),m(yt)!=="svelte-1f4f5kp"&&(yt.innerHTML=An),at.forEach(r),q.forEach(r),ho=s(e),f(Oe.$$.fragment,e),mo=s(e),A=c(e,"DIV",{class:!0});var dt=N(A);f(Ge.$$.fragment,dt),Bo=s(dt),kt=c(dt,"P",{"data-svelte-h":!0}),m(kt)!=="svelte-1dobm33"&&(kt.textContent=Qn),dt.forEach(r),uo=s(e),Q=c(e,"DIV",{class:!0});var it=N(Q);f(Ze.$$.fragment,it),Ro=s(it),vt=c(it,"P",{"data-svelte-h":!0}),m(vt)!=="svelte-k12cko"&&(vt.textContent=Yn),it.forEach(r),fo=s(e),Y=c(e,"DIV",{class:!0});var ct=N(Y);f(We.$$.fragment,ct),Eo=s(ct),wt=c(ct,"P",{"data-svelte-h":!0}),m(wt)!=="svelte-1kt4x95"&&(wt.textContent=Kn),ct.forEach(r),_o=s(e),K=c(e,"DIV",{class:!0});var lt=N(K);f(He.$$.fragment,lt),Do=s(lt),Mt=c(lt,"P",{"data-svelte-h":!0}),m(Mt)!=="svelte-1kt4x95"&&(Mt.textContent=es),lt.forEach(r),go=s(e),f(Se.$$.fragment,e),To=s(e),C=c(e,"DIV",{class:!0});var J=N(C);f(Ve.$$.fragment,J),Xo=s(J),Nt=c(J,"P",{"data-svelte-h":!0}),m(Nt)!=="svelte-1rsvet7"&&(Nt.innerHTML=ts),Ao=s(J),$t=c(J,"P",{"data-svelte-h":!0}),m($t)!=="svelte-jbq9y7"&&($t.innerHTML=os),Qo=s(J),zt=c(J,"P",{"data-svelte-h":!0}),m(zt)!=="svelte-1707pv8"&&(zt.innerHTML=ns),Yo=s(J),I=c(J,"DIV",{class:!0});var fe=N(I);f(Be.$$.fragment,fe),Ko=s(fe),Pt=c(fe,"P",{"data-svelte-h":!0}),m(Pt)!=="svelte-1kgt3hk"&&(Pt.innerHTML=ss),en=s(fe),f(re.$$.fragment,fe),tn=s(fe),f(ae.$$.fragment,fe),fe.forEach(r),J.forEach(r),bo=s(e),f(Re.$$.fragment,e),yo=s(e),P=c(e,"DIV",{class:!0});var W=N(P);f(Ee.$$.fragment,W),on=s(W),xt=c(W,"P",{"data-svelte-h":!0}),m(xt)!=="svelte-zg9td7"&&(xt.innerHTML=rs),nn=s(W),qt=c(W,"P",{"data-svelte-h":!0}),m(qt)!=="svelte-jbq9y7"&&(qt.innerHTML=as),sn=s(W),Ct=c(W,"P",{"data-svelte-h":!0}),m(Ct)!=="svelte-1707pv8"&&(Ct.innerHTML=ds),rn=s(W),Ft=c(W,"P",{"data-svelte-h":!0}),m(Ft)!=="svelte-ptbbcs"&&(Ft.innerHTML=is),an=s(W),L=c(W,"DIV",{class:!0});var _e=N(L);f(De.$$.fragment,_e),dn=s(_e),jt=c(_e,"P",{"data-svelte-h":!0}),m(jt)!=="svelte-1jxpdyo"&&(jt.innerHTML=cs),cn=s(_e),f(de.$$.fragment,_e),ln=s(_e),f(ie.$$.fragment,_e),_e.forEach(r),W.forEach(r),ko=s(e),f(Xe.$$.fragment,e),vo=s(e),x=c(e,"DIV",{class:!0});var H=N(x);f(Ae.$$.fragment,H),pn=s(H),Jt=c(H,"P",{"data-svelte-h":!0}),m(Jt)!=="svelte-1cg6q77"&&(Jt.innerHTML=ls),hn=s(H),Ut=c(H,"P",{"data-svelte-h":!0}),m(Ut)!=="svelte-jbq9y7"&&(Ut.innerHTML=ps),mn=s(H),It=c(H,"P",{"data-svelte-h":!0}),m(It)!=="svelte-1707pv8"&&(It.innerHTML=hs),un=s(H),Lt=c(H,"P",{"data-svelte-h":!0}),m(Lt)!=="svelte-ptbbcs"&&(Lt.innerHTML=ms),fn=s(H),O=c(H,"DIV",{class:!0});var ge=N(O);f(Qe.$$.fragment,ge),_n=s(ge),Ot=c(ge,"P",{"data-svelte-h":!0}),m(Ot)!=="svelte-10uor1g"&&(Ot.innerHTML=us),gn=s(ge),f(ce.$$.fragment,ge),Tn=s(ge),f(le.$$.fragment,ge),ge.forEach(r),H.forEach(r),wo=s(e),f(Ye.$$.fragment,e),Mo=s(e),F=c(e,"DIV",{class:!0});var D=N(F);f(Ke.$$.fragment,D),bn=s(D),Gt=c(D,"P",{"data-svelte-h":!0}),m(Gt)!=="svelte-1pfm6e1"&&(Gt.innerHTML=fs),yn=s(D),Zt=c(D,"P",{"data-svelte-h":!0}),m(Zt)!=="svelte-jbq9y7"&&(Zt.innerHTML=_s),kn=s(D),Wt=c(D,"P",{"data-svelte-h":!0}),m(Wt)!=="svelte-1707pv8"&&(Wt.innerHTML=gs),vn=s(D),G=c(D,"DIV",{class:!0});var Te=N(G);f(et.$$.fragment,Te),wn=s(Te),Ht=c(Te,"P",{"data-svelte-h":!0}),m(Ht)!=="svelte-jv6i7o"&&(Ht.innerHTML=Ts),Mn=s(Te),f(pe.$$.fragment,Te),Nn=s(Te),f(he.$$.fragment,Te),Te.forEach(r),D.forEach(r),No=s(e),f(tt.$$.fragment,e),$o=s(e),j=c(e,"DIV",{class:!0});var X=N(j);f(ot.$$.fragment,X),$n=s(X),St=c(X,"P",{"data-svelte-h":!0}),m(St)!=="svelte-1mibsxe"&&(St.innerHTML=bs),zn=s(X),Vt=c(X,"P",{"data-svelte-h":!0}),m(Vt)!=="svelte-jbq9y7"&&(Vt.innerHTML=ys),Pn=s(X),Bt=c(X,"P",{"data-svelte-h":!0}),m(Bt)!=="svelte-1707pv8"&&(Bt.innerHTML=ks),xn=s(X),Z=c(X,"DIV",{class:!0});var be=N(Z);f(nt.$$.fragment,be),qn=s(be),Rt=c(be,"P",{"data-svelte-h":!0}),m(Rt)!=="svelte-1igt9k8"&&(Rt.innerHTML=vs),Cn=s(be),f(me.$$.fragment,be),Fn=s(be),f(ue.$$.fragment,be),be.forEach(r),X.forEach(r),zo=s(e),f(st.$$.fragment,e),Po=s(e),Xt=c(e,"P",{}),N(Xt).forEach(r),this.h()},h(){M(o,"name","hf:doc:metadata"),M(o,"content",Ws),M(v,"class","flex flex-wrap space-x-1"),M(V,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),M(E,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),M(oe,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),M(U,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),M(se,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),M(z,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),M(A,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),M(Q,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),M(Y,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),M(K,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),M(I,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),M(C,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),M(L,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),M(P,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),M(O,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),M(x,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),M(G,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),M(F,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),M(Z,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),M(j,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8")},m(e,d){t(document.head,o),l(e,y,d),l(e,p,d),l(e,h,d),_(k,e,d),l(e,a,d),l(e,v,d),l(e,Qt,d),_(ye,e,d),l(e,Yt,d),l(e,ke,d),l(e,Kt,d),l(e,ve,d),l(e,eo,d),l(e,we,d),l(e,to,d),l(e,Me,d),l(e,oo,d),l(e,Ne,d),l(e,no,d),_($e,e,d),l(e,so,d),l(e,ze,d),l(e,ro,d),_(Pe,e,d),l(e,ao,d),l(e,xe,d),l(e,io,d),_(qe,e,d),l(e,co,d),l(e,V,d),_(Ce,V,null),t(V,qo),t(V,pt),t(V,Co),t(V,ht),l(e,lo,d),_(Fe,e,d),l(e,po,d),l(e,z,d),_(je,z,null),t(z,Fo),t(z,mt),t(z,jo),t(z,ut),t(z,Jo),t(z,E),_(Je,E,null),t(E,Uo),t(E,ft),t(E,Io),t(E,_t),t(z,Lo),t(z,oe),_(Ue,oe,null),t(oe,Oo),t(oe,gt),t(z,Go),t(z,U),_(Ie,U,null),t(U,Zo),t(U,Tt),t(U,Wo),_(ne,U,null),t(U,Ho),t(U,bt),t(z,So),t(z,se),_(Le,se,null),t(se,Vo),t(se,yt),l(e,ho,d),_(Oe,e,d),l(e,mo,d),l(e,A,d),_(Ge,A,null),t(A,Bo),t(A,kt),l(e,uo,d),l(e,Q,d),_(Ze,Q,null),t(Q,Ro),t(Q,vt),l(e,fo,d),l(e,Y,d),_(We,Y,null),t(Y,Eo),t(Y,wt),l(e,_o,d),l(e,K,d),_(He,K,null),t(K,Do),t(K,Mt),l(e,go,d),_(Se,e,d),l(e,To,d),l(e,C,d),_(Ve,C,null),t(C,Xo),t(C,Nt),t(C,Ao),t(C,$t),t(C,Qo),t(C,zt),t(C,Yo),t(C,I),_(Be,I,null),t(I,Ko),t(I,Pt),t(I,en),_(re,I,null),t(I,tn),_(ae,I,null),l(e,bo,d),_(Re,e,d),l(e,yo,d),l(e,P,d),_(Ee,P,null),t(P,on),t(P,xt),t(P,nn),t(P,qt),t(P,sn),t(P,Ct),t(P,rn),t(P,Ft),t(P,an),t(P,L),_(De,L,null),t(L,dn),t(L,jt),t(L,cn),_(de,L,null),t(L,ln),_(ie,L,null),l(e,ko,d),_(Xe,e,d),l(e,vo,d),l(e,x,d),_(Ae,x,null),t(x,pn),t(x,Jt),t(x,hn),t(x,Ut),t(x,mn),t(x,It),t(x,un),t(x,Lt),t(x,fn),t(x,O),_(Qe,O,null),t(O,_n),t(O,Ot),t(O,gn),_(ce,O,null),t(O,Tn),_(le,O,null),l(e,wo,d),_(Ye,e,d),l(e,Mo,d),l(e,F,d),_(Ke,F,null),t(F,bn),t(F,Gt),t(F,yn),t(F,Zt),t(F,kn),t(F,Wt),t(F,vn),t(F,G),_(et,G,null),t(G,wn),t(G,Ht),t(G,Mn),_(pe,G,null),t(G,Nn),_(he,G,null),l(e,No,d),_(tt,e,d),l(e,$o,d),l(e,j,d),_(ot,j,null),t(j,$n),t(j,St),t(j,zn),t(j,Vt),t(j,Pn),t(j,Bt),t(j,xn),t(j,Z),_(nt,Z,null),t(Z,qn),t(Z,Rt),t(Z,Cn),_(me,Z,null),t(Z,Fn),_(ue,Z,null),l(e,zo,d),_(st,e,d),l(e,Po,d),l(e,Xt,d),xo=!0},p(e,[d]){const ee={};d&2&&(ee.$$scope={dirty:d,ctx:e}),ne.$set(ee);const q={};d&2&&(q.$$scope={dirty:d,ctx:e}),re.$set(q);const te={};d&2&&(te.$$scope={dirty:d,ctx:e}),ae.$set(te);const rt={};d&2&&(rt.$$scope={dirty:d,ctx:e}),de.$set(rt);const B={};d&2&&(B.$$scope={dirty:d,ctx:e}),ie.$set(B);const at={};d&2&&(at.$$scope={dirty:d,ctx:e}),ce.$set(at);const dt={};d&2&&(dt.$$scope={dirty:d,ctx:e}),le.$set(dt);const it={};d&2&&(it.$$scope={dirty:d,ctx:e}),pe.$set(it);const ct={};d&2&&(ct.$$scope={dirty:d,ctx:e}),he.$set(ct);const lt={};d&2&&(lt.$$scope={dirty:d,ctx:e}),me.$set(lt);const J={};d&2&&(J.$$scope={dirty:d,ctx:e}),ue.$set(J)},i(e){xo||(g(k.$$.fragment,e),g(ye.$$.fragment,e),g($e.$$.fragment,e),g(Pe.$$.fragment,e),g(qe.$$.fragment,e),g(Ce.$$.fragment,e),g(Fe.$$.fragment,e),g(je.$$.fragment,e),g(Je.$$.fragment,e),g(Ue.$$.fragment,e),g(Ie.$$.fragment,e),g(ne.$$.fragment,e),g(Le.$$.fragment,e),g(Oe.$$.fragment,e),g(Ge.$$.fragment,e),g(Ze.$$.fragment,e),g(We.$$.fragment,e),g(He.$$.fragment,e),g(Se.$$.fragment,e),g(Ve.$$.fragment,e),g(Be.$$.fragment,e),g(re.$$.fragment,e),g(ae.$$.fragment,e),g(Re.$$.fragment,e),g(Ee.$$.fragment,e),g(De.$$.fragment,e),g(de.$$.fragment,e),g(ie.$$.fragment,e),g(Xe.$$.fragment,e),g(Ae.$$.fragment,e),g(Qe.$$.fragment,e),g(ce.$$.fragment,e),g(le.$$.fragment,e),g(Ye.$$.fragment,e),g(Ke.$$.fragment,e),g(et.$$.fragment,e),g(pe.$$.fragment,e),g(he.$$.fragment,e),g(tt.$$.fragment,e),g(ot.$$.fragment,e),g(nt.$$.fragment,e),g(me.$$.fragment,e),g(ue.$$.fragment,e),g(st.$$.fragment,e),xo=!0)},o(e){T(k.$$.fragment,e),T(ye.$$.fragment,e),T($e.$$.fragment,e),T(Pe.$$.fragment,e),T(qe.$$.fragment,e),T(Ce.$$.fragment,e),T(Fe.$$.fragment,e),T(je.$$.fragment,e),T(Je.$$.fragment,e),T(Ue.$$.fragment,e),T(Ie.$$.fragment,e),T(ne.$$.fragment,e),T(Le.$$.fragment,e),T(Oe.$$.fragment,e),T(Ge.$$.fragment,e),T(Ze.$$.fragment,e),T(We.$$.fragment,e),T(He.$$.fragment,e),T(Se.$$.fragment,e),T(Ve.$$.fragment,e),T(Be.$$.fragment,e),T(re.$$.fragment,e),T(ae.$$.fragment,e),T(Re.$$.fragment,e),T(Ee.$$.fragment,e),T(De.$$.fragment,e),T(de.$$.fragment,e),T(ie.$$.fragment,e),T(Xe.$$.fragment,e),T(Ae.$$.fragment,e),T(Qe.$$.fragment,e),T(ce.$$.fragment,e),T(le.$$.fragment,e),T(Ye.$$.fragment,e),T(Ke.$$.fragment,e),T(et.$$.fragment,e),T(pe.$$.fragment,e),T(he.$$.fragment,e),T(tt.$$.fragment,e),T(ot.$$.fragment,e),T(nt.$$.fragment,e),T(me.$$.fragment,e),T(ue.$$.fragment,e),T(st.$$.fragment,e),xo=!1},d(e){e&&(r(y),r(p),r(h),r(a),r(v),r(Qt),r(Yt),r(ke),r(Kt),r(ve),r(eo),r(we),r(to),r(Me),r(oo),r(Ne),r(no),r(so),r(ze),r(ro),r(ao),r(xe),r(io),r(co),r(V),r(lo),r(po),r(z),r(ho),r(mo),r(A),r(uo),r(Q),r(fo),r(Y),r(_o),r(K),r(go),r(To),r(C),r(bo),r(yo),r(P),r(ko),r(vo),r(x),r(wo),r(Mo),r(F),r(No),r($o),r(j),r(zo),r(Po),r(Xt)),r(o),b(k,e),b(ye,e),b($e,e),b(Pe,e),b(qe,e),b(Ce),b(Fe,e),b(je),b(Je),b(Ue),b(Ie),b(ne),b(Le),b(Oe,e),b(Ge),b(Ze),b(We),b(He),b(Se,e),b(Ve),b(Be),b(re),b(ae),b(Re,e),b(Ee),b(De),b(de),b(ie),b(Xe,e),b(Ae),b(Qe),b(ce),b(le),b(Ye,e),b(Ke),b(et),b(pe),b(he),b(tt,e),b(ot),b(nt),b(me),b(ue),b(st,e)}}}const Ws='{"title":"ProphetNet","local":"prophetnet","sections":[{"title":"Overview","local":"overview","sections":[],"depth":2},{"title":"Usage tips","local":"usage-tips","sections":[],"depth":2},{"title":"Resources","local":"resources","sections":[],"depth":2},{"title":"ProphetNetConfig","local":"transformers.ProphetNetConfig","sections":[],"depth":2},{"title":"ProphetNetTokenizer","local":"transformers.ProphetNetTokenizer","sections":[],"depth":2},{"title":"ProphetNet specific outputs","local":"transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqLMOutput","sections":[],"depth":2},{"title":"ProphetNetModel","local":"transformers.ProphetNetModel","sections":[],"depth":2},{"title":"ProphetNetEncoder","local":"transformers.ProphetNetEncoder","sections":[],"depth":2},{"title":"ProphetNetDecoder","local":"transformers.ProphetNetDecoder","sections":[],"depth":2},{"title":"ProphetNetForConditionalGeneration","local":"transformers.ProphetNetForConditionalGeneration","sections":[],"depth":2},{"title":"ProphetNetForCausalLM","local":"transformers.ProphetNetForCausalLM","sections":[],"depth":2}],"depth":1}';function Hs(w){return Ms(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class As extends Ns{constructor(o){super(),$s(this,o,Hs,Zs,ws,{})}}export{As as component};
