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--- |
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language: |
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- en |
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license: apache-2.0 |
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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:132020 |
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- loss:MultipleNegativesRankingLoss |
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base_model: pyrac/rse_gestion_durable |
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widget: |
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- source_sentence: Les chemins extérieurs n'avaient pas de garde-corps pour sécuriser |
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les déplacements. |
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sentences: |
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- Les mesures d'accessibilité pour les personnes handicapées sont bien respectées |
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dans l'établissement |
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- Cette chambre nous a vraiment déçus, rien n’était comme on l’espérait. |
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- L'absence de barres d'appui rend cet hôtel moins pratique pour les personnes en |
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situation de handicap |
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- source_sentence: Les rampes étaient inclinées de façon inconfortable, limitant leur |
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accessibilité. |
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sentences: |
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- La chambre PMR n’était pas adaptée à notre confort, on en est ressortis frustrés. |
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- Les ascenseurs de l'hôtel sont trop petits pour un fauteuil roulant ce qui complique |
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les déplacements |
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- La sécurité est assurée avec un gardien présent. |
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- source_sentence: L'absence d'indication en braille était regrettable. |
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sentences: |
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- Parking sécurisé avec gardien. |
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- Il est difficile de trouver un restaurant accessible aux fauteuils roulants dans |
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cette ville car beaucoup d'entre eux ont des escaliers |
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- difficiles à parcourir en fauteuil roulant. |
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- source_sentence: Il n'y avait aucun plan en braille pour les visiteurs malvoyants. |
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sentences: |
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- Bon rapport qualité-prix pour le stationnement. |
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- Aucune signalétique tactile n'était présente dans les espaces communs. |
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- Le théâtre est mal conçu pour les fauteuils roulants et il est difficile de trouver |
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des places adaptées |
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- source_sentence: L'absence de cheminement accessible a rendu la visite difficile |
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pour ma famille. |
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sentences: |
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- Le lavabo était trop haut, ce qui le rendait inutilisable pour les personnes en |
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fauteuil roulant. |
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- Ce n’était pas du tout ce qu’on voulait, cette chambre a gâché notre séjour. |
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- L'hôtel n'offre pas assez de chambres adaptées aux fauteuils roulants et il est |
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difficile de réserver à la dernière minute |
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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: MPNet base trained on AllNLI triplets |
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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: all nli dev |
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type: all-nli-dev |
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metrics: |
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- type: cosine_accuracy |
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value: 1.0 |
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name: Cosine Accuracy |
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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: all nli test |
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type: all-nli-test |
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metrics: |
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- type: cosine_accuracy |
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value: 1.0 |
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name: Cosine Accuracy |
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--- |
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# MPNet base trained on AllNLI triplets |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [pyrac/rse_gestion_durable](https://huggingface.co/pyrac/rse_gestion_durable). 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:** [pyrac/rse_gestion_durable](https://huggingface.co/pyrac/rse_gestion_durable) <!-- at revision fc41501961df3b7a70af7a014df8e12349918dcf --> |
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- **Maximum Sequence Length:** 128 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:** en |
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- **License:** apache-2.0 |
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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': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel |
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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("pyrac/rse_handicap") |
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# Run inference |
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sentences = [ |
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"L'absence de cheminement accessible a rendu la visite difficile pour ma famille.", |
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"L'hôtel n'offre pas assez de chambres adaptées aux fauteuils roulants et il est difficile de réserver à la dernière minute", |
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'Ce n’était pas du tout ce qu’on voulait, cette chambre a gâché notre séjour.', |
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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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## Evaluation |
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### Metrics |
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#### Triplet |
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* Datasets: `all-nli-dev` and `all-nli-test` |
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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 | all-nli-dev | all-nli-test | |
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|:--------------------|:------------|:-------------| |
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| **cosine_accuracy** | **1.0** | **1.0** | |
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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: 132,020 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: 7 tokens</li><li>mean: 20.24 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 23.96 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 19.23 tokens</li><li>max: 31 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | negative | |
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|:----------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------| |
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| <code>Les panneaux d'indication manquaient de braille, ce qui pénalisait les malvoyants.</code> | <code>Hôtel bien situé et accessible mais l'absence de barres d'appui est un vrai point négatif</code> | <code>On a eu une chambre mal entretenue, rien ne fonctionnait comme il fallait.</code> | |
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| <code>Le cheminement extérieur n'était pas praticable pour les fauteuils roulants électriques.</code> | <code>Aucun confort dans cette chambre PMR, elle n'était absolument pas adaptée à nos besoins.</code> | <code>On a eu une chambre trop bruyante, c’était vraiment une mauvaise expérience.</code> | |
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| <code>Il n'y avait pas de signalisation concernant l'accessibilité pour les personnes à mobilité réduite</code> | <code>La chambre adaptée aux fauteuils roulants est spacieuse et permet de circuler sans aucune difficulté</code> | <code>Se retrouver dans cette chambre sans avoir rien demandé, ça a vraiment gâché notre séjour.</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": 20.0, |
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"similarity_fct": "cos_sim" |
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} |
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``` |
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### Evaluation Dataset |
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#### Unnamed Dataset |
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* Size: 16,502 evaluation 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: 7 tokens</li><li>mean: 20.06 tokens</li><li>max: 55 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 24.09 tokens</li><li>max: 55 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 18.96 tokens</li><li>max: 31 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | negative | |
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|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------| |
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| <code>Le cheminement accessible était interrompu par des obstacles imprévus.</code> | <code>étaient inaccessibles depuis un fauteuil roulant.</code> | <code>On n’a pas du tout aimé la chambre, l’équipement était dépassé et inconfortable.</code> | |
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| <code>était bien adapté pour les fauteuils roulants.</code> | <code>On a été choqués de nous retrouver dans une chambre accessible pour handicapé, c’était inconfortable.</code> | <code>Cette chambre était en tout point décevante, ça ne correspondait pas du tout à ce qu’on avait espéré.</code> | |
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| <code>Leur sensibilisation sur le handicap auditif semblaient insuffisantes.</code> | <code>Le cheminement était bien signalé avec des panneaux visuels et tactiles.</code> | <code>On s’est retrouvés dans une chambre qu’on n’avait pas demandée, ça nous a déstabilisés.</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": 20.0, |
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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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- `eval_strategy`: steps |
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- `per_device_train_batch_size`: 64 |
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- `per_device_eval_batch_size`: 64 |
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- `num_train_epochs`: 1 |
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- `warmup_ratio`: 0.1 |
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- `bf16`: True |
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- `batch_sampler`: no_duplicates |
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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`: steps |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 64 |
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- `per_device_eval_batch_size`: 64 |
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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`: 1 |
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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`: 0.1 |
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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`: True |
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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`: no_duplicates |
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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 | Validation Loss | all-nli-dev_cosine_accuracy | all-nli-test_cosine_accuracy | |
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|:------:|:----:|:-------------:|:---------------:|:---------------------------:|:----------------------------:| |
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| 0.0485 | 100 | 4.6964 | 4.0874 | 1.0 | - | |
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| 0.0969 | 200 | 4.1044 | 4.0497 | 1.0 | - | |
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| 0.1454 | 300 | 4.0817 | 4.0305 | 1.0 | - | |
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| 0.1939 | 400 | 4.0734 | 4.0310 | 1.0 | - | |
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| 0.2424 | 500 | 4.0587 | 4.0209 | 1.0 | - | |
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| 0.2908 | 600 | 4.0625 | 4.0180 | 1.0 | - | |
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| 0.3393 | 700 | 4.053 | 4.0201 | 1.0 | - | |
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| 0.3878 | 800 | 4.0607 | 4.0116 | 1.0 | - | |
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| 0.4363 | 900 | 4.0511 | 4.0078 | 1.0 | - | |
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| 0.4847 | 1000 | 4.0433 | 4.0087 | 1.0 | - | |
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| 0.5332 | 1100 | 4.0385 | 4.0080 | 1.0 | - | |
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| 0.5817 | 1200 | 4.0413 | 4.0055 | 1.0 | - | |
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| 0.6302 | 1300 | 4.044 | 4.0016 | 1.0 | - | |
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| 0.6786 | 1400 | 4.0385 | 4.0010 | 1.0 | - | |
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| 0.7271 | 1500 | 4.037 | 3.9974 | 1.0 | - | |
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| 0.7756 | 1600 | 4.0364 | 3.9965 | 1.0 | - | |
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| 0.8240 | 1700 | 4.0337 | 3.9988 | 1.0 | - | |
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| 0.8725 | 1800 | 4.0362 | 3.9965 | 1.0 | - | |
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| 0.9210 | 1900 | 4.0293 | 3.9964 | 1.0 | - | |
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| 0.9695 | 2000 | 4.0317 | 3.9947 | 1.0 | - | |
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| -1 | -1 | - | - | - | 1.0 | |
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### Framework Versions |
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- Python: 3.12.3 |
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- Sentence Transformers: 3.4.1 |
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- Transformers: 4.48.3 |
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- PyTorch: 2.6.0+cu124 |
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- Accelerate: 1.3.0 |
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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", |
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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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