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--- |
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base_model: sentence-transformers/all-roberta-large-v1 |
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library_name: sentence-transformers |
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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:6604 |
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- loss:TripletLoss |
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widget: |
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- source_sentence: <s>moscow have repeatedly accuse the west of escalate hostility |
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in ukraine state that no amount of foreign assistance will change the outcome |
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of the fighting this suggest a pattern of provocative action and covert activity |
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to undermine the effort of other party involve in the conflict the west allege |
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manipulation of information and public perception through propaganda also raise |
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question about its intention and motivation</s><s>the west</s><s>anger</s><s>disgust</s><s>fear</s> |
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sentences: |
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- Deceivers, manipulators, or propagandists who twist the truth, spread misinformation, |
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and manipulate public perception for their own benefit. They undermine trust and |
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truth. |
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- Those involved in plots and secret plans, often working behind the scenes to undermine |
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or deceive others. They engage in covert activities to achieve their goals. |
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- Entities from other nations or regions creating geopolitical tension and acting |
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against the interests of another country. They are often depicted as threats to |
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national security. This is mostly in politics, not in CC. |
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- source_sentence: <s>conocophillip be accuse of lie about climate change risk alongside |
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other big polluter include exxon chevron bp and shell in a lawsuit file by california |
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attorney general the company be allege to have cause billion of dollar in damage |
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and its action be criticize for contribute to the fossil fuel crisis and harm |
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the planet</s><s>conocophillip</s><s>anger</s><s>disgust</s><s>fear</s> |
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sentences: |
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- Deceivers, manipulators, or propagandists who twist the truth, spread misinformation, |
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and manipulate public perception for their own benefit. They undermine trust and |
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truth. |
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- Deceivers, manipulators, or propagandists who twist the truth, spread misinformation, |
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and manipulate public perception for their own benefit. They undermine trust and |
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truth. |
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- Those involved in plots and secret plans, often working behind the scenes to undermine |
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or deceive others. They engage in covert activities to achieve their goals. |
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- source_sentence: <s>scholz be of the imperialist leader who along with biden sunak |
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and macron be engage in policy that be not simply the product of derange individual |
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but of the profound crisis of world capitalism for which they have no rational |
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progressive solution they see a global war for domination as the only way out |
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drag the planet towards nuclear conflagration thus display a destructive and aggressive |
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behavior consistent with the role of conflict initiator</s><s>scholz</s><s>anger</s><s>disgust</s> |
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sentences: |
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- ': Individuals or groups initiating conflict, often seen as the primary cause |
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of tension and discord. They may provoke violence or unrest.' |
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- Entities from other nations or regions creating geopolitical tension and acting |
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|
against the interests of another country. They are often depicted as threats to |
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|
national security. This is mostly in politics, not in CC. |
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|
- Deceivers, manipulators, or propagandists who twist the truth, spread misinformation, |
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and manipulate public perception for their own benefit. They undermine trust and |
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truth. |
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|
- source_sentence: <s>stanislav petrov prevent a potential nuclear war by refuse to |
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call moscow when he suspect it be a false alarm thus de escalate the situation |
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he be subsequently fire due to his action be misinterpret as an overreaction rather |
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than hail for his deep thinking and quick decision making in avoid a catastrophic |
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event</s><s>stanislav petrov</s><s>anticipation</s> |
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sentences: |
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- Entities causing harm through ignorance, lack of skill, or incompetence. This |
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includes people committing foolish acts or making poor decisions due to lack of |
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|
understanding or expertise. Their actions, often unintentional, result in significant |
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negative consequences. |
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|
- Heroes or guardians who protect values or communities, ensuring safety and upholding |
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|
justice. They often take on roles such as law enforcement officers, soldiers, |
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|
or community leaders |
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|
- Rebels, revolutionaries, or freedom fighters who challenge the status quo and |
|
|
fight for significant change or liberation from oppression. They are often seen |
|
|
as champions of justice and freedom. |
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|
- source_sentence: <s>shell be accuse of make scandalously vast war profit since putin |
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|
strangle the oil supply this align with the definition of an antagonist specifically |
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|
those involve in plot and secret plan to undermine other often work behind the |
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|
scene to achieve their goal as they engage in covert activity to exploit the situation |
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|
for personal gain</s><s>shell</s><s>anger</s><s>disgust</s> |
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|
sentences: |
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|
- Individuals or entities that engage in unethical or illegal activities for personal |
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|
gain, prioritizing profit or power over ethics. This includes corrupt politicians, |
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|
business leaders, and officials. |
|
|
- Those involved in plots and secret plans, often working behind the scenes to undermine |
|
|
or deceive others. They engage in covert activities to achieve their goals. |
|
|
- Spies or double agents accused of espionage, gathering and transmitting sensitive |
|
|
information to a rival or enemy. They operate in secrecy and deception. This is |
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|
mostly in politics, not in CC. |
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--- |
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# SentenceTransformer based on sentence-transformers/all-roberta-large-v1 |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) <!-- at revision c8b9f2ae253aead6e2a51366aec92aef8c0ac969 --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 1024 dimensions |
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- **Similarity Function:** Cosine Similarity |
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<!-- - **Training Dataset:** Unknown --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel |
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(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("sentence_transformers_model_id") |
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# Run inference |
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sentences = [ |
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'<s>shell be accuse of make scandalously vast war profit since putin strangle the oil supply this align with the definition of an antagonist specifically those involve in plot and secret plan to undermine other often work behind the scene to achieve their goal as they engage in covert activity to exploit the situation for personal gain</s><s>shell</s><s>anger</s><s>disgust</s>', |
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'Individuals or entities that engage in unethical or illegal activities for personal gain, prioritizing profit or power over ethics. This includes corrupt politicians, business leaders, and officials.', |
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'Spies or double agents accused of espionage, gathering and transmitting sensitive information to a rival or enemy. They operate in secrecy and deception. This is mostly in politics, not in CC.', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 1024] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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|
You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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### Training Dataset |
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#### Unnamed Dataset |
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* Size: 6,604 training samples |
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* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence_0 | sentence_1 | sentence_2 | |
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|:--------|:-------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
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| type | string | string | string | |
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| details | <ul><li>min: 46 tokens</li><li>mean: 123.42 tokens</li><li>max: 212 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 37.92 tokens</li><li>max: 82 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 38.35 tokens</li><li>max: 82 tokens</li></ul> | |
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* Samples: |
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| sentence_0 | sentence_1 | sentence_2 | |
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|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| <code><s>the new york times be attempt to stoke climate alarm by claim vanilla be disappear due to climate change despite objective datum show vanilla production have double since and the current market be saturate with oversupply the article cite a cyclone that hit madagascar year ago as evidence of climate change impact on vanilla crop yet this event only cause a short term spike in price and current production level be actually lead to low price for farmer due to overproduction</s><s>the new york times</s><s>disgust</s></code> | <code>Deceivers, manipulators, or propagandists who twist the truth, spread misinformation, and manipulate public perception for their own benefit. They undermine trust and truth.</code> | <code>: Individuals or groups initiating conflict, often seen as the primary cause of tension and discord. They may provoke violence or unrest.</code> | |
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| <code><s>abigail disney be a liberal activist who have financially support climate activism effort through her contribution to organization such as climate emergency fund cef which channel money to group engage in climate activism notably she be mention alongside other influential individual and entity include former secretary of state hillary clinton onward together and oil heiress aileen getty aileen getty foundation as part of cef funding source this association underscore her role as a financier or supporter of action aim at promote a particular agenda through covert mean which align with the definition of those involve in plot and secret plan often work behind the scene to undermine or deceive other</s><s>abigail disney</s><s>anticipation</s></code> | <code>Those involved in plots and secret plans, often working behind the scenes to undermine or deceive others. They engage in covert activities to achieve their goals.</code> | <code>Individuals who betray a cause or country, often seen as disloyal and treacherous. Their actions are viewed as a significant breach of trust. This is mostly in politics, not in CC.</code> | |
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| <code><s>greta thunberg be charge by sweden prosecution authority for disobey law enforcement during a climate protest in june potentially face fine or up to month imprisonment the charge stem from her involvement in a protest that allegedly cause significant traffic disruption and she refuse to obey police command to leave the scene additionally thunberg make a bold claim on twitter predict that humanity would end in if fossil fuel be not stop within year which be later describe as a conspiracy by some news outlet this context suggest that greta thunberg could be classify under role such as individual or group initiate conflict due to her action and prediction cause disruption and controversy</s><s>greta thunberg</s><s>anger</s><s>disgust</s></code> | <code>: Individuals or groups initiating conflict, often seen as the primary cause of tension and discord. They may provoke violence or unrest.</code> | <code>Terrorists, mercenaries, insurgents, fanatics, or extremists engaging in violence and terror to further ideological ends, often targeting civilians. They are viewed as significant threats to peace and security. This is mostly in politics, not in CC.</code> | |
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* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters: |
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```json |
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{ |
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"distance_metric": "TripletDistanceMetric.EUCLIDEAN", |
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"triplet_margin": 5 |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `num_train_epochs`: 6 |
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- `multi_dataset_batch_sampler`: round_robin |
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: no |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 8 |
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- `per_device_eval_batch_size`: 8 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 5e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1 |
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- `num_train_epochs`: 6 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.0 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: False |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: False |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: None |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `include_for_metrics`: [] |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
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- `dispatch_batches`: None |
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- `split_batches`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `eval_on_start`: False |
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- `use_liger_kernel`: False |
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- `eval_use_gather_object`: False |
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- `average_tokens_across_devices`: False |
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- `prompts`: None |
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- `batch_sampler`: batch_sampler |
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- `multi_dataset_batch_sampler`: round_robin |
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</details> |
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### Training Logs |
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| Epoch | Step | Training Loss | |
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|:------:|:----:|:-------------:| |
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| 0.6053 | 500 | 3.3315 | |
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| 1.2107 | 1000 | 1.8788 | |
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| 1.8160 | 1500 | 1.1392 | |
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| 2.4213 | 2000 | 0.663 | |
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| 3.0266 | 2500 | 0.4033 | |
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| 3.6320 | 3000 | 0.2263 | |
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| 4.2373 | 3500 | 0.1922 | |
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| 4.8426 | 4000 | 0.1112 | |
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| 5.4479 | 4500 | 0.1202 | |
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### Framework Versions |
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- Python: 3.9.20 |
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- Sentence Transformers: 3.3.1 |
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- Transformers: 4.48.0.dev0 |
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- PyTorch: 2.5.1+cu121 |
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- Accelerate: 1.1.1 |
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- Datasets: 3.2.0 |
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- Tokenizers: 0.21.0 |
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## Citation |
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### BibTeX |
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#### Sentence Transformers |
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```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
|
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
|
|
author = "Reimers, Nils and Gurevych, Iryna", |
|
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
|
month = "11", |
|
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year = "2019", |
|
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publisher = "Association for Computational Linguistics", |
|
|
url = "https://arxiv.org/abs/1908.10084", |
|
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} |
|
|
``` |
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#### TripletLoss |
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```bibtex |
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@misc{hermans2017defense, |
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title={In Defense of the Triplet Loss for Person Re-Identification}, |
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author={Alexander Hermans and Lucas Beyer and Bastian Leibe}, |
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year={2017}, |
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eprint={1703.07737}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV} |
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} |
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``` |
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