Create README.md

README.md

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+---
+language:
+- en
+pipeline_tag: text2text-generation
+---
+
+```python
+from transformers import T5ForConditionalGeneration
+from transformers import T5TokenizerFast as T5Tokenizer
+import pandas as pd
+model = "svjack/comet-atomic-en"
+device = "cpu"
+#device = "cuda:0"
+tokenizer = T5Tokenizer.from_pretrained(model)
+model = T5ForConditionalGeneration.from_pretrained(model).to(device).eval()
+
+NEED_PREFIX = 'What are the necessary preconditions for the next event?'
+EFFECT_PREFIX = 'What could happen after the next event?'
+INTENT_PREFIX = 'What is the motivation for the next event?'
+REACT_PREFIX = 'What are your feelings after the following event?'
+
+
+event = "X had a big meal."
+for prefix in [NEED_PREFIX, EFFECT_PREFIX, INTENT_PREFIX, REACT_PREFIX]:
+    prompt = "{}{}".format(prefix, event)
+    encode = tokenizer(prompt, return_tensors='pt').to(device)
+    answer = model.generate(encode.input_ids,
+                           max_length = 128,
+        num_beams=2,
+        top_p = 0.95,
+        top_k = 50,
+        repetition_penalty = 2.5,
+        length_penalty=1.0,
+        early_stopping=True,
+                           )[0]
+    decoded = tokenizer.decode(answer, skip_special_tokens=True)
+    print(prompt, "\n---Answer：", decoded, "----\n")
+```
+
+</br>
+
+```json
+What are the necessary preconditions for the next event?X had a big meal. 
+---Answer： X goes shopping at the supermarket ----
+
+What could happen after the next event?X had a big meal. 
+---Answer： X gets fat ----
+
+What is the motivation for the next event?X had a big meal. 
+---Answer： X wants to eat ----
+
+What are your feelings after the following event?X had a big meal. 
+---Answer： X tastes good ----
+```