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--- |
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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:18963 |
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- loss:MultipleNegativesRankingLoss |
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base_model: sentence-transformers/paraphrase-mpnet-base-v2 |
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widget: |
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- source_sentence: If the comatose man had previously expressed a desire to be euthanized |
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in such a situation, respecting his autonomy would support euthanasia. |
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sentences: |
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- If the comatose man had previously expressed a desire for euthanasia in such circumstances, |
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there may be a duty to respect his autonomy, which would support the action. |
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- If the man is believed to be suffering in his comatose state or there is a significant |
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burden on his family, there may be a duty to alleviate suffering that supports |
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euthanasia. |
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- As a living being, the rat may warrant a duty of care from humans, which may include |
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providing it with appropriate medical treatment or humane euthanasia in case of |
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suffering. |
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- source_sentence: Resisting authoritarianism can defend individual freedom and undermine |
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oppressive regimes. |
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sentences: |
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- Resisting authoritarianism can be a means of exercising the right to free speech |
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and expression, which may be suppressed by the government. |
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- If retreating serves to protect the lives of soldiers and civilians, then it upholds |
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the value of the duty to protect. |
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- Resisting authoritarianism could result in negative consequences for safety and |
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security if violence is used to resist. |
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- source_sentence: Saving someone upholds their fundamental right to life, as it prevents |
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them from experiencing harm or death. |
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sentences: |
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- Donating the money to charity has the potential to benefit those in need and can |
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be seen as fulfilling a duty to improve the well-being of others. |
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- Saving someone may preserve their freedom and ability to make choices in their |
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life. |
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- If saving someone involves protecting their body from injury or harm, their right |
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to bodily integrity is respected. |
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- source_sentence: Helping those in need, such as a starving person, promotes a sense |
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of community and responsibility towards fellow humans. |
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sentences: |
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- We have a moral responsibility to treat others with respect and dignity, regardless |
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of their race. Hanging out with black people allows for the opportunity to demonstrate |
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this respect. |
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- A starving person's right to life is at stake, and providing them with food can |
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help protect this fundamental right. |
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- Providing aid and resources to someone in need is an expression of the duty to |
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promote the well-being of others. |
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- source_sentence: The marriage of Baptiste and Hannah demonstrates their commitment |
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to sharing their lives and supporting one another. |
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sentences: |
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- Helping others may be a moral duty, but using unethical means like cheating goes |
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against other moral principles. |
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- If the marriage brings happiness to Baptiste and Hannah, then they are pursuing |
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their right to happiness. |
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- By getting married, Baptiste and Hannah take on a duty to care for each other, |
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both emotionally and materially. |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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--- |
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# SentenceTransformer based on sentence-transformers/paraphrase-mpnet-base-v2 |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) on the train dataset. It maps sentences & paragraphs to a 768-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/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) <!-- at revision 0446e4ee4c8cef910c1b1dd164b6276d66bd47c0 --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 768 dimensions |
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- **Similarity Function:** Cosine Similarity |
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- **Training Dataset:** |
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- train |
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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: MPNetModel |
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(1): Pooling({'word_embedding_dimension': 768, '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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'The marriage of Baptiste and Hannah demonstrates their commitment to sharing their lives and supporting one another.', |
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'By getting married, Baptiste and Hannah take on a duty to care for each other, both emotionally and materially.', |
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'If the marriage brings happiness to Baptiste and Hannah, then they are pursuing their right to happiness.', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 768] |
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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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#### train |
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* Dataset: train |
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* Size: 18,963 training samples |
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* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | anchor | positive | negative | |
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|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
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| type | string | string | string | |
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| details | <ul><li>min: 10 tokens</li><li>mean: 25.92 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 28.31 tokens</li><li>max: 60 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 28.69 tokens</li><li>max: 67 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | negative | |
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|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| <code>Saving the group of people from harm by diverting the trolley supports the value of preserving life.</code> | <code>The group of people tied to the tracks have a right to life, which is protected when the trolley is diverted to save them.</code> | <code>Diverting the trolley reduces overall harm by preventing the deaths of many people at the cost of one person's life.</code> | |
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| <code>The bake sale could be seen as an expression of support for a particular cause, and the right to freely express oneself and associate with others who share the same views is important.</code> | <code>The bake sale might be seen as a form of protest or support for a specific cause, and individuals have the right to engage in peaceful protest or show support.</code> | <code>If the bake sale directly or indirectly promotes religious discrimination, this can infringe on the fundamental right of individuals to be free from discrimination or harm due to their religious beliefs.</code> | |
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| <code>Children have a right to life, and saving them from danger upholds this right.</code> | <code>Children should be protected from harm, abuse, and danger, and saving them ensures this right is respected.</code> | <code>Children have a right to grow up with access to healthcare, education, and a nurturing environment. Saving them may help secure these rights.</code> | |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
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```json |
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{ |
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"scale": 40, |
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"similarity_fct": "cos_sim" |
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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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- `overwrite_output_dir`: True |
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- `per_device_train_batch_size`: 32 |
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- `learning_rate`: 2.1456771788455288e-05 |
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- `num_train_epochs`: 2 |
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- `warmup_ratio`: 0.03254893834779507 |
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- `fp16`: True |
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- `dataloader_num_workers`: 4 |
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- `remove_unused_columns`: False |
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: True |
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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`: 32 |
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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`: 2.1456771788455288e-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`: 1.0 |
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- `num_train_epochs`: 2 |
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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`: 0.03254893834779507 |
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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`: True |
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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`: 4 |
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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`: False |
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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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- `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`: proportional |
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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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| 0.0337 | 20 | 0.2448 | |
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| 0.0675 | 40 | 0.1918 | |
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| 0.1012 | 60 | 0.14 | |
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| 0.1349 | 80 | 0.186 | |
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| 0.1686 | 100 | 0.1407 | |
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| 0.2024 | 120 | 0.1672 | |
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| 0.2361 | 140 | 0.1832 | |
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| 0.2698 | 160 | 0.116 | |
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| 0.3035 | 180 | 0.1341 | |
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| 0.3373 | 200 | 0.2118 | |
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| 0.3710 | 220 | 0.1274 | |
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| 0.4047 | 240 | 0.1993 | |
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| 0.4384 | 260 | 0.1561 | |
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| 0.4722 | 280 | 0.1517 | |
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| 0.5059 | 300 | 0.1635 | |
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| 0.5396 | 320 | 0.1646 | |
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| 0.5734 | 340 | 0.1337 | |
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| 0.6071 | 360 | 0.1406 | |
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| 0.6408 | 380 | 0.1114 | |
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| 0.6745 | 400 | 0.1314 | |
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| 0.7083 | 420 | 0.1481 | |
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| 0.7420 | 440 | 0.1932 | |
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| 0.7757 | 460 | 0.1568 | |
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| 0.8094 | 480 | 0.1319 | |
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| 0.8432 | 500 | 0.1536 | |
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| 0.8769 | 520 | 0.1462 | |
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| 0.9106 | 540 | 0.1336 | |
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| 0.9444 | 560 | 0.1453 | |
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| 0.9781 | 580 | 0.2005 | |
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| 1.0118 | 600 | 0.1265 | |
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| 1.0455 | 620 | 0.0702 | |
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| 1.0793 | 640 | 0.0739 | |
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| 1.1130 | 660 | 0.049 | |
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| 1.1467 | 680 | 0.0613 | |
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| 1.1804 | 700 | 0.0663 | |
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| 1.2142 | 720 | 0.0726 | |
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| 1.2479 | 740 | 0.0822 | |
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| 1.2816 | 760 | 0.0651 | |
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| 1.3153 | 780 | 0.0603 | |
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| 1.3491 | 800 | 0.0468 | |
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| 1.3828 | 820 | 0.061 | |
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| 1.4165 | 840 | 0.0891 | |
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| 1.4503 | 860 | 0.0607 | |
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| 1.4840 | 880 | 0.0673 | |
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| 1.5177 | 900 | 0.0728 | |
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| 1.5514 | 920 | 0.065 | |
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| 1.5852 | 940 | 0.0824 | |
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| 1.6189 | 960 | 0.0695 | |
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| 1.6526 | 980 | 0.0626 | |
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| 1.6863 | 1000 | 0.0525 | |
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| 1.7201 | 1020 | 0.0482 | |
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| 1.7538 | 1040 | 0.0968 | |
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| 1.7875 | 1060 | 0.0717 | |
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| 1.8212 | 1080 | 0.0704 | |
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| 1.8550 | 1100 | 0.0666 | |
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| 1.8887 | 1120 | 0.0841 | |
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| 1.9224 | 1140 | 0.0682 | |
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| 1.9562 | 1160 | 0.0584 | |
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| 1.9899 | 1180 | 0.0423 | |
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### Framework Versions |
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- Python: 3.9.21 |
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- Sentence Transformers: 4.1.0 |
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- Transformers: 4.52.4 |
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- PyTorch: 2.6.0+cu124 |
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- Accelerate: 1.5.2 |
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- Datasets: 3.4.1 |
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- Tokenizers: 0.21.1 |
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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", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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#### MultipleNegativesRankingLoss |
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```bibtex |
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@misc{henderson2017efficient, |
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title={Efficient Natural Language Response Suggestion for Smart Reply}, |
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author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
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year={2017}, |
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eprint={1705.00652}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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