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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:32351 |
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- loss:TripletLoss |
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base_model: sentence-transformers/all-mpnet-base-v2 |
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widget: |
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- source_sentence: Genetic conditions that cause nutritional deficiencies can prevent |
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a person from removing meat from their diet. |
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sentences: |
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- Ante un estado que no quiere hablar del tema, para Cataluña, solo es posible seguir |
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su propio camino por otras vías. |
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- Retinol deficiency is a genetically pre-disposed condition that prevents conversion |
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beta-carotene to Vitamin A \(retinol\) in humans. Since plants have no retinol |
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\(only beta-carotene\), humans with this condition cannot have a vegan diet, only |
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one with animal products. |
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- People with hemochromatosis \(a genetic condition\) can benefit greatly from a |
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vegan diet, due to the lower absorbing non-heme iron in plants \(compared to heme |
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iron in meat\). |
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- source_sentence: 'The definition of veganism is: "A way of living which seeks to |
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exclude, as far as is possible and practicable, all forms of exploitation of, |
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and cruelty to, animals for food, clothing or any other purpose." In the \(unlikely\) |
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case of survival or health concerns, the "as far as possible and practicable" |
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clause makes it possible for such persons to be considered vegan as they would |
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have no alternative options.' |
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sentences: |
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- Veganism is not solely about diet. A person can still choose to live in accordance |
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with vegan values, such as by avoiding animal circuses and leather/fur products. |
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- It's easier to regulate established companies in a legal market than it is in |
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the black market. Any issue would be with bad regulations not legalization. |
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- That definition is too vague. There are different definitions of veganism, many |
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of which are not compatible with using animals in any circumstances. In a way |
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we are all vegan depending on how easy you believe it is to reach all the necessary |
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nutrition in your city harming as few animals as possible. |
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- source_sentence: Adding coding to the school curriculum means that something else |
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must be left out. |
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sentences: |
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- Coding skills are much needed in today's job market. |
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- Cataluña saldría de la UE con efectos económicos desastrosos. |
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- Teaching coding effectively is impossible unless teachers are trained appropriately |
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first. |
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- source_sentence: Animals have innate, individual rights, which are taken away when |
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they are killed or made to suffer. |
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sentences: |
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- Animals have a desire to live. |
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- Uno de los ejemplos más claros es la falta de inversión reiterada al Corredor |
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Mediterráneo \(Algeciras-Valencia-Barcelona-Francia\), prioritario para la UE |
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y Catalunya, pero relegado a algo residual por el estado Español. |
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- A vegan society would equate humans rights with animal rights, which would make |
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society worse off overall. |
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- source_sentence: The sorts of people likely to lash out against affirmative action |
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policies probably already hold negative views towards racial minorities. |
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sentences: |
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- The Far Right movement sees the inequality affirmative action addresses not as |
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a problem to be solved, but as an outcome to be desired. |
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- There are plenty of people who hold a positive view towards racial minorities |
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and still oppose affirmative action. |
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- Research has shown that college degrees have less economic utility for people |
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from low socio-economic backgrounds. |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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metrics: |
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- cosine_accuracy |
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model-index: |
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- name: SentenceTransformer based on sentence-transformers/all-mpnet-base-v2 |
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results: |
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- task: |
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type: triplet |
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name: Triplet |
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dataset: |
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name: Unknown |
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type: unknown |
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metrics: |
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- type: cosine_accuracy |
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value: 0.9264069199562073 |
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name: Cosine Accuracy |
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- type: cosine_accuracy |
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value: 0.9161931872367859 |
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name: Cosine Accuracy |
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--- |
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# SentenceTransformer based on sentence-transformers/all-mpnet-base-v2 |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2). 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/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) <!-- at revision 9a3225965996d404b775526de6dbfe85d3368642 --> |
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- **Maximum Sequence Length:** 384 tokens |
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- **Output Dimensionality:** 768 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': 384, '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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(2): Normalize() |
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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 sorts of people likely to lash out against affirmative action policies probably already hold negative views towards racial minorities.', |
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'The Far Right movement sees the inequality affirmative action addresses not as a problem to be solved, but as an outcome to be desired.', |
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'There are plenty of people who hold a positive view towards racial minorities and still oppose affirmative action.', |
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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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### 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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### 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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## Evaluation |
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### Metrics |
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#### Triplet |
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* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) |
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| Metric | Value | |
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|:--------------------|:-----------| |
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| **cosine_accuracy** | **0.9264** | |
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#### Triplet |
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* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) |
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| Metric | Value | |
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|:--------------------|:-----------| |
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| **cosine_accuracy** | **0.9162** | |
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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: 32,351 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: 6 tokens</li><li>mean: 30.94 tokens</li><li>max: 160 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 40.8 tokens</li><li>max: 180 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 44.95 tokens</li><li>max: 162 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | negative | |
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|:----------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| <code>La soberanía y la decisión sobre la unidad de España residen en el conjunto de España.</code> | <code>Apostar por un proceso de secesión es ir en contra de la globalización, la corriente histórica que vivimos.</code> | <code>Los tratados internacionales \(incluido el Tratado de La Unión Europea\) no serían aplicables a Cataluña como estado independiente, por lo que su permanencia en Europa podría verse interrumpida.</code> | |
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| <code>La soberanía y la decisión sobre la unidad de España residen en el conjunto de España.</code> | <code>Para sentar un precedente en conflictos de autodeterminación en el mundo.</code> | <code>La independencia de Cataluña afectaría negativamente a la economía de España.</code> | |
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| <code>La soberanía y la decisión sobre la unidad de España residen en el conjunto de España.</code> | <code>Para terminar con el trato injusto que recibe Cataluña al ser parte de España.</code> | <code>Por definición, cualquier nacionalismo es malo ya que crea divisiones artificiales y es fuente de conflictos.</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.COSINE", |
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"triplet_margin": 0.3 |
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} |
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``` |
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### Training Hyperparameters |
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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`: 1.0 |
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- `num_train_epochs`: 3.0 |
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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`: proportional |
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</details> |
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### Training Logs |
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| Epoch | Step | Training Loss | cosine_accuracy | |
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|:------:|:-----:|:-------------:|:---------------:| |
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| 0.1236 | 500 | 0.1872 | - | |
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| 0.2473 | 1000 | 0.1954 | - | |
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| 0.3709 | 1500 | 0.1854 | - | |
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| 0.4946 | 2000 | 0.1891 | - | |
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| 0.6182 | 2500 | 0.181 | - | |
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| 0.7418 | 3000 | 0.1794 | - | |
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| 0.8655 | 3500 | 0.1815 | - | |
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| 0.9891 | 4000 | 0.1736 | - | |
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| 1.1128 | 4500 | 0.1342 | - | |
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| 1.2364 | 5000 | 0.1297 | - | |
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| 1.3600 | 5500 | 0.1318 | - | |
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| 1.4837 | 6000 | 0.1255 | - | |
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| 1.6073 | 6500 | 0.128 | - | |
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| 1.7310 | 7000 | 0.1233 | - | |
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| 1.8546 | 7500 | 0.1221 | - | |
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| 1.9782 | 8000 | 0.1232 | - | |
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| 2.1019 | 8500 | 0.0841 | - | |
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| 2.2255 | 9000 | 0.0757 | - | |
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| 2.3492 | 9500 | 0.0764 | - | |
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| 2.4728 | 10000 | 0.0761 | - | |
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| 2.5964 | 10500 | 0.0726 | - | |
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| 2.7201 | 11000 | 0.0644 | - | |
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| 2.8437 | 11500 | 0.073 | - | |
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| 2.9674 | 12000 | 0.0725 | - | |
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| -1 | -1 | - | 0.9162 | |
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### Framework Versions |
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- Python: 3.11.11 |
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- Sentence Transformers: 3.4.1 |
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- Transformers: 4.48.3 |
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- PyTorch: 2.5.1+cu124 |
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- Accelerate: 1.3.0 |
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- Datasets: 3.3.2 |
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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", |
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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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#### 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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