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--- |
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language: |
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- en |
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library_name: sentence-transformers |
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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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- dataset_size:100K<n<1M |
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- loss:MultipleNegativesRankingLoss |
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base_model: microsoft/deberta-v3-xsmall |
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metrics: |
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- pearson_cosine |
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- spearman_cosine |
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- pearson_manhattan |
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- spearman_manhattan |
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- pearson_euclidean |
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- spearman_euclidean |
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- pearson_dot |
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- spearman_dot |
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- pearson_max |
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- spearman_max |
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- cosine_accuracy |
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- cosine_accuracy_threshold |
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- cosine_f1 |
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- cosine_f1_threshold |
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- cosine_precision |
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- cosine_recall |
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- cosine_ap |
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- dot_accuracy |
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- dot_accuracy_threshold |
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- dot_f1 |
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- dot_f1_threshold |
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- dot_precision |
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- dot_recall |
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- dot_ap |
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- manhattan_accuracy |
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- manhattan_accuracy_threshold |
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- manhattan_f1 |
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- manhattan_f1_threshold |
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- manhattan_precision |
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- manhattan_recall |
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- manhattan_ap |
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- euclidean_accuracy |
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- euclidean_accuracy_threshold |
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- euclidean_f1 |
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- euclidean_f1_threshold |
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- euclidean_precision |
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- euclidean_recall |
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- euclidean_ap |
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- max_accuracy |
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- max_accuracy_threshold |
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- max_f1 |
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- max_f1_threshold |
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- max_precision |
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- max_recall |
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- max_ap |
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widget: |
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- source_sentence: No, monsieur. |
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sentences: |
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- Yes, sir. |
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- Look, there's a legend here. |
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- All models are subject to analysis. |
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- source_sentence: She shrugged. |
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sentences: |
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- She acted like it didn't matter. |
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- He felt bad for doubting her. |
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- Jacques Teti movies are my favorite. |
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- source_sentence: We can think. |
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sentences: |
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- We need to think. |
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- A man is on his way to work. |
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- Her favorite candy is chocolate. |
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- source_sentence: He loved her. |
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sentences: |
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- She was loved by him. |
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- The person is playing rugby. |
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- All models are subject to analysis. |
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- source_sentence: in each square |
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sentences: |
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- It is widespread. |
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- A young girl flips an omelet. |
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- He charged Jon with a knife. |
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pipeline_tag: sentence-similarity |
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model-index: |
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- name: SentenceTransformer based on microsoft/deberta-v3-xsmall |
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results: |
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- task: |
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type: semantic-similarity |
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name: Semantic Similarity |
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dataset: |
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name: sts dev |
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type: sts-dev |
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metrics: |
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- type: pearson_cosine |
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value: 0.7972304062599285 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.8069984848350104 |
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name: Spearman Cosine |
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- type: pearson_manhattan |
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value: 0.8078500467589406 |
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name: Pearson Manhattan |
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- type: spearman_manhattan |
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value: 0.8072286629818308 |
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name: Spearman Manhattan |
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- type: pearson_euclidean |
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value: 0.8083747460970299 |
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name: Pearson Euclidean |
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- type: spearman_euclidean |
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value: 0.807329204776433 |
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name: Spearman Euclidean |
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- type: pearson_dot |
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value: 0.7028547677818588 |
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name: Pearson Dot |
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- type: spearman_dot |
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value: 0.690944321229592 |
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name: Spearman Dot |
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- type: pearson_max |
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value: 0.8083747460970299 |
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name: Pearson Max |
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- type: spearman_max |
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value: 0.807329204776433 |
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name: Spearman Max |
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- task: |
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type: binary-classification |
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name: Binary Classification |
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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: 0.677155205095155 |
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name: Cosine Accuracy |
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- type: cosine_accuracy_threshold |
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value: 0.7285403609275818 |
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name: Cosine Accuracy Threshold |
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- type: cosine_f1 |
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value: 0.7186860786908915 |
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name: Cosine F1 |
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- type: cosine_f1_threshold |
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value: 0.6111028790473938 |
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name: Cosine F1 Threshold |
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- type: cosine_precision |
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value: 0.6110485933503836 |
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name: Cosine Precision |
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- type: cosine_recall |
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value: 0.8723528552650796 |
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name: Cosine Recall |
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- type: cosine_ap |
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value: 0.73917897685454 |
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name: Cosine Ap |
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- type: dot_accuracy |
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value: 0.6382591553567367 |
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name: Dot Accuracy |
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- type: dot_accuracy_threshold |
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value: 228.40408325195312 |
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name: Dot Accuracy Threshold |
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- type: dot_f1 |
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value: 0.706771220880316 |
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name: Dot F1 |
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- type: dot_f1_threshold |
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value: 177.3942108154297 |
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name: Dot F1 Threshold |
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- type: dot_precision |
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value: 0.5811370481927711 |
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name: Dot Precision |
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- type: dot_recall |
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value: 0.9017087775668176 |
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name: Dot Recall |
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- type: dot_ap |
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value: 0.6903597943138529 |
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name: Dot Ap |
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- type: manhattan_accuracy |
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value: 0.6635074683448328 |
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name: Manhattan Accuracy |
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- type: manhattan_accuracy_threshold |
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value: 174.62747192382812 |
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name: Manhattan Accuracy Threshold |
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- type: manhattan_f1 |
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value: 0.7054413268204022 |
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name: Manhattan F1 |
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- type: manhattan_f1_threshold |
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value: 232.6788330078125 |
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name: Manhattan F1 Threshold |
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- type: manhattan_precision |
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value: 0.5771911887721908 |
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name: Manhattan Precision |
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- type: manhattan_recall |
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value: 0.906966554695487 |
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name: Manhattan Recall |
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- type: manhattan_ap |
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value: 0.7282119371967055 |
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name: Manhattan Ap |
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- type: euclidean_accuracy |
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value: 0.6650997042990371 |
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name: Euclidean Accuracy |
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- type: euclidean_accuracy_threshold |
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value: 13.422540664672852 |
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name: Euclidean Accuracy Threshold |
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- type: euclidean_f1 |
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value: 0.7067711563398544 |
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name: Euclidean F1 |
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- type: euclidean_f1_threshold |
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value: 17.634807586669922 |
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name: Euclidean F1 Threshold |
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- type: euclidean_precision |
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value: 0.5755739210284665 |
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name: Euclidean Precision |
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- type: euclidean_recall |
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value: 0.9154374178472323 |
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name: Euclidean Recall |
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- type: euclidean_ap |
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value: 0.730311832588485 |
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name: Euclidean Ap |
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- type: max_accuracy |
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value: 0.677155205095155 |
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name: Max Accuracy |
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- type: max_accuracy_threshold |
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value: 228.40408325195312 |
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name: Max Accuracy Threshold |
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- type: max_f1 |
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value: 0.7186860786908915 |
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name: Max F1 |
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- type: max_f1_threshold |
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value: 232.6788330078125 |
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name: Max F1 Threshold |
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- type: max_precision |
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value: 0.6110485933503836 |
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name: Max Precision |
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- type: max_recall |
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value: 0.9154374178472323 |
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name: Max Recall |
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- type: max_ap |
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value: 0.73917897685454 |
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name: Max Ap |
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--- |
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|
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# SentenceTransformer based on microsoft/deberta-v3-xsmall |
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|
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/deberta-v3-xsmall](https://huggingface.co/microsoft/deberta-v3-xsmall) on the [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli) dataset. It maps sentences & paragraphs to a 384-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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|
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [microsoft/deberta-v3-xsmall](https://huggingface.co/microsoft/deberta-v3-xsmall) <!-- at revision 4b419818330868dff6a60ad3e6b1c730f8b8c0c6 --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 384 tokens |
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- **Similarity Function:** Cosine Similarity |
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- **Training Dataset:** |
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- [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli) |
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- **Language:** en |
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<!-- - **License:** Unknown --> |
|
|
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### Model Sources |
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|
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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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|
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### Full Model Architecture |
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|
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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: DebertaV2Model |
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(1): Pooling({'word_embedding_dimension': 384, '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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|
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## Usage |
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|
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### Direct Usage (Sentence Transformers) |
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|
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First install the Sentence Transformers library: |
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|
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```bash |
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pip install -U sentence-transformers |
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``` |
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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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|
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# Download from the 🤗 Hub |
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model = SentenceTransformer("bobox/DeBERTaV3-xSmall-SentenceTransformer-0.03") |
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# Run inference |
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sentences = [ |
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'in each square', |
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'It is widespread.', |
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'A young girl flips an omelet.', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 384] |
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|
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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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<!-- |
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### Direct Usage (Transformers) |
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|
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<details><summary>Click to see the direct usage in Transformers</summary> |
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|
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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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|
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You can finetune this model on your own dataset. |
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|
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<details><summary>Click to expand</summary> |
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|
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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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|
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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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|
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### Metrics |
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|
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#### Semantic Similarity |
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* Dataset: `sts-dev` |
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* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
|
|
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| Metric | Value | |
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|:--------------------|:----------| |
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| pearson_cosine | 0.7972 | |
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| **spearman_cosine** | **0.807** | |
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| pearson_manhattan | 0.8079 | |
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| spearman_manhattan | 0.8072 | |
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| pearson_euclidean | 0.8084 | |
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| spearman_euclidean | 0.8073 | |
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| pearson_dot | 0.7029 | |
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| spearman_dot | 0.6909 | |
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| pearson_max | 0.8084 | |
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| spearman_max | 0.8073 | |
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|
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#### Binary Classification |
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|
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* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) |
|
|
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| Metric | Value | |
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|:-----------------------------|:-----------| |
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| cosine_accuracy | 0.6772 | |
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| cosine_accuracy_threshold | 0.7285 | |
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| cosine_f1 | 0.7187 | |
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| cosine_f1_threshold | 0.6111 | |
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| cosine_precision | 0.611 | |
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| cosine_recall | 0.8724 | |
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| cosine_ap | 0.7392 | |
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| dot_accuracy | 0.6383 | |
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| dot_accuracy_threshold | 228.4041 | |
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| dot_f1 | 0.7068 | |
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| dot_f1_threshold | 177.3942 | |
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| dot_precision | 0.5811 | |
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| dot_recall | 0.9017 | |
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| dot_ap | 0.6904 | |
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| manhattan_accuracy | 0.6635 | |
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| manhattan_accuracy_threshold | 174.6275 | |
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| manhattan_f1 | 0.7054 | |
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| manhattan_f1_threshold | 232.6788 | |
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| manhattan_precision | 0.5772 | |
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| manhattan_recall | 0.907 | |
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| manhattan_ap | 0.7282 | |
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| euclidean_accuracy | 0.6651 | |
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| euclidean_accuracy_threshold | 13.4225 | |
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| euclidean_f1 | 0.7068 | |
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| euclidean_f1_threshold | 17.6348 | |
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| euclidean_precision | 0.5756 | |
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| euclidean_recall | 0.9154 | |
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| euclidean_ap | 0.7303 | |
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| max_accuracy | 0.6772 | |
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| max_accuracy_threshold | 228.4041 | |
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| max_f1 | 0.7187 | |
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| max_f1_threshold | 232.6788 | |
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| max_precision | 0.611 | |
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| max_recall | 0.9154 | |
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| **max_ap** | **0.7392** | |
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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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<!-- |
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### Recommendations |
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|
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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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|
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#### stanfordnlp/snli |
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|
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* Dataset: [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli) at [cdb5c3d](https://huggingface.co/datasets/stanfordnlp/snli/tree/cdb5c3d5eed6ead6e5a341c8e56e669bb666725b) |
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* Size: 314,315 training samples |
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* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence1 | sentence2 | label | |
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|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-----------------------------| |
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| type | string | string | int | |
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| details | <ul><li>min: 5 tokens</li><li>mean: 16.62 tokens</li><li>max: 62 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.46 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>0: 100.00%</li></ul> | |
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* Samples: |
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| sentence1 | sentence2 | label | |
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|:---------------------------------------------------------------------------|:-------------------------------------------------|:---------------| |
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| <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is outdoors, on a horse.</code> | <code>0</code> | |
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| <code>Children smiling and waving at camera</code> | <code>There are children present</code> | <code>0</code> | |
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| <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | <code>0</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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|
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### Evaluation Dataset |
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|
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#### sentence-transformers/stsb |
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|
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* Dataset: [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) at [ab7a5ac](https://huggingface.co/datasets/sentence-transformers/stsb/tree/ab7a5ac0e35aa22088bdcf23e7fd99b220e53308) |
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* Size: 1,500 evaluation samples |
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* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> |
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* Approximate statistics based on the first 1000 samples: |
|
| | sentence1 | sentence2 | score | |
|
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| |
|
| type | string | string | float | |
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| details | <ul><li>min: 5 tokens</li><li>mean: 14.77 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.74 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.47</li><li>max: 1.0</li></ul> | |
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* Samples: |
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| sentence1 | sentence2 | score | |
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|:--------------------------------------------------|:------------------------------------------------------|:------------------| |
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| <code>A man with a hard hat is dancing.</code> | <code>A man wearing a hard hat is dancing.</code> | <code>1.0</code> | |
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| <code>A young child is riding a horse.</code> | <code>A child is riding a horse.</code> | <code>0.95</code> | |
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| <code>A man is feeding a mouse to a snake.</code> | <code>The man is feeding a mouse to the snake.</code> | <code>1.0</code> | |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
|
```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 |
|
#### 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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- `learning_rate`: 7.5e-05 |
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- `num_train_epochs`: 2 |
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- `warmup_ratio`: 0.25 |
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- `save_safetensors`: False |
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- `fp16`: True |
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- `push_to_hub`: True |
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- `hub_model_id`: bobox/DeBERTaV3-xSmall-SentenceTransformer-0.03n |
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- `hub_strategy`: checkpoint |
|
|
|
#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
|
|
|
- `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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- `learning_rate`: 7.5e-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`: 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`: 0.25 |
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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`: False |
|
- `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 |
|
- `jit_mode_eval`: False |
|
- `use_ipex`: False |
|
- `bf16`: False |
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- `fp16`: True |
|
- `fp16_opt_level`: O1 |
|
- `half_precision_backend`: auto |
|
- `bf16_full_eval`: False |
|
- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
|
- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
|
- `debug`: [] |
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- `dataloader_drop_last`: False |
|
- `dataloader_num_workers`: 0 |
|
- `dataloader_prefetch_factor`: None |
|
- `past_index`: -1 |
|
- `disable_tqdm`: False |
|
- `remove_unused_columns`: True |
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- `label_names`: None |
|
- `load_best_model_at_end`: False |
|
- `ignore_data_skip`: False |
|
- `fsdp`: [] |
|
- `fsdp_min_num_params`: 0 |
|
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
|
- `fsdp_transformer_layer_cls_to_wrap`: None |
|
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
|
- `deepspeed`: None |
|
- `label_smoothing_factor`: 0.0 |
|
- `optim`: adamw_torch |
|
- `optim_args`: None |
|
- `adafactor`: False |
|
- `group_by_length`: False |
|
- `length_column_name`: length |
|
- `ddp_find_unused_parameters`: None |
|
- `ddp_bucket_cap_mb`: None |
|
- `ddp_broadcast_buffers`: False |
|
- `dataloader_pin_memory`: True |
|
- `dataloader_persistent_workers`: False |
|
- `skip_memory_metrics`: True |
|
- `use_legacy_prediction_loop`: False |
|
- `push_to_hub`: True |
|
- `resume_from_checkpoint`: None |
|
- `hub_model_id`: bobox/DeBERTaV3-xSmall-SentenceTransformer-0.03n |
|
- `hub_strategy`: checkpoint |
|
- `hub_private_repo`: False |
|
- `hub_always_push`: False |
|
- `gradient_checkpointing`: False |
|
- `gradient_checkpointing_kwargs`: None |
|
- `include_inputs_for_metrics`: False |
|
- `eval_do_concat_batches`: True |
|
- `fp16_backend`: auto |
|
- `push_to_hub_model_id`: None |
|
- `push_to_hub_organization`: None |
|
- `mp_parameters`: |
|
- `auto_find_batch_size`: False |
|
- `full_determinism`: False |
|
- `torchdynamo`: None |
|
- `ray_scope`: last |
|
- `ddp_timeout`: 1800 |
|
- `torch_compile`: False |
|
- `torch_compile_backend`: None |
|
- `torch_compile_mode`: None |
|
- `dispatch_batches`: None |
|
- `split_batches`: None |
|
- `include_tokens_per_second`: False |
|
- `include_num_input_tokens_seen`: False |
|
- `neftune_noise_alpha`: None |
|
- `optim_target_modules`: None |
|
- `batch_eval_metrics`: False |
|
- `batch_sampler`: batch_sampler |
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
|
|
|
### Training Logs |
|
| Epoch | Step | Training Loss | loss | max_ap | sts-dev_spearman_cosine | |
|
|:------:|:----:|:-------------:|:------:|:------:|:-----------------------:| |
|
| None | 0 | - | 3.7624 | 0.5721 | 0.4168 | |
|
| 0.0501 | 246 | 3.3825 | - | - | - | |
|
| 0.1002 | 492 | 1.8307 | - | - | - | |
|
| 0.1500 | 737 | - | 1.0084 | 0.7024 | - | |
|
| 0.1502 | 738 | 1.055 | - | - | - | |
|
| 0.2003 | 984 | 0.7961 | - | - | - | |
|
| 0.2504 | 1230 | 0.6859 | - | - | - | |
|
| 0.3001 | 1474 | - | 0.7410 | 0.7191 | - | |
|
| 0.3005 | 1476 | 0.5914 | - | - | - | |
|
| 0.3506 | 1722 | 0.5324 | - | - | - | |
|
| 0.4007 | 1968 | 0.5077 | - | - | - | |
|
| 0.4501 | 2211 | - | 0.6152 | 0.7144 | - | |
|
| 0.4507 | 2214 | 0.4647 | - | - | - | |
|
| 0.5008 | 2460 | 0.4443 | - | - | - | |
|
| 0.5509 | 2706 | 0.4169 | - | - | - | |
|
| 0.6002 | 2948 | - | 0.5820 | 0.7207 | - | |
|
| 0.6010 | 2952 | 0.3831 | - | - | - | |
|
| 0.6511 | 3198 | 0.393 | - | - | - | |
|
| 0.7011 | 3444 | 0.3654 | - | - | - | |
|
| 0.7502 | 3685 | - | 0.5284 | 0.7264 | - | |
|
| 0.7512 | 3690 | 0.344 | - | - | - | |
|
| 0.8013 | 3936 | 0.3336 | - | - | - | |
|
| 0.8514 | 4182 | 0.3382 | - | - | - | |
|
| 0.9002 | 4422 | - | 0.4911 | 0.7294 | - | |
|
| 0.9015 | 4428 | 0.3182 | - | - | - | |
|
| 0.9515 | 4674 | 0.3213 | - | - | - | |
|
| 1.0016 | 4920 | 0.3032 | - | - | - | |
|
| 1.0503 | 5159 | - | 0.4777 | 0.7325 | - | |
|
| 1.0517 | 5166 | 0.2526 | - | - | - | |
|
| 1.1018 | 5412 | 0.2652 | - | - | - | |
|
| 1.1519 | 5658 | 0.2538 | - | - | - | |
|
| 1.2003 | 5896 | - | 0.4569 | 0.7331 | - | |
|
| 1.2020 | 5904 | 0.2454 | - | - | - | |
|
| 1.2520 | 6150 | 0.2528 | - | - | - | |
|
| 1.3021 | 6396 | 0.2448 | - | - | - | |
|
| 1.3504 | 6633 | - | 0.4334 | 0.7370 | - | |
|
| 1.3522 | 6642 | 0.2282 | - | - | - | |
|
| 1.4023 | 6888 | 0.2295 | - | - | - | |
|
| 1.4524 | 7134 | 0.2313 | - | - | - | |
|
| 1.5004 | 7370 | - | 0.4237 | 0.7342 | - | |
|
| 1.5024 | 7380 | 0.2218 | - | - | - | |
|
| 1.5525 | 7626 | 0.2246 | - | - | - | |
|
| 1.6026 | 7872 | 0.218 | - | - | - | |
|
| 1.6504 | 8107 | - | 0.4102 | 0.7388 | - | |
|
| 1.6527 | 8118 | 0.2095 | - | - | - | |
|
| 1.7028 | 8364 | 0.2114 | - | - | - | |
|
| 1.7529 | 8610 | 0.2063 | - | - | - | |
|
| 1.8005 | 8844 | - | 0.4075 | 0.7370 | - | |
|
| 1.8029 | 8856 | 0.1968 | - | - | - | |
|
| 1.8530 | 9102 | 0.2061 | - | - | - | |
|
| 1.9031 | 9348 | 0.2089 | - | - | - | |
|
| 1.9505 | 9581 | - | 0.3978 | 0.7395 | - | |
|
| 1.9532 | 9594 | 0.2005 | - | - | - | |
|
| 2.0 | 9824 | - | 0.3963 | 0.7392 | - | |
|
| None | 0 | - | 1.5506 | - | 0.8070 | |
|
|
|
|
|
### Framework Versions |
|
- Python: 3.10.12 |
|
- Sentence Transformers: 3.0.0 |
|
- Transformers: 4.41.2 |
|
- PyTorch: 2.3.0+cu121 |
|
- Accelerate: 0.30.1 |
|
- Datasets: 2.19.2 |
|
- Tokenizers: 0.19.1 |
|
|
|
## Citation |
|
|
|
### BibTeX |
|
|
|
#### Sentence Transformers |
|
```bibtex |
|
@inproceedings{reimers-2019-sentence-bert, |
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
|
author = "Reimers, Nils and Gurevych, Iryna", |
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
month = "11", |
|
year = "2019", |
|
publisher = "Association for Computational Linguistics", |
|
url = "https://arxiv.org/abs/1908.10084", |
|
} |
|
``` |
|
|
|
#### MultipleNegativesRankingLoss |
|
```bibtex |
|
@misc{henderson2017efficient, |
|
title={Efficient Natural Language Response Suggestion for Smart Reply}, |
|
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}, |
|
year={2017}, |
|
eprint={1705.00652}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CL} |
|
} |
|
``` |
|
|
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