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--- |
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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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- feature-extraction |
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- sentence-similarity |
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language: en |
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datasets: |
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- quora |
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- embedding-data/WikiAnswers |
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- flax-sentence-embeddings/stackexchange_xml |
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license: cc-by-nc-sa-4.0 |
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--- |
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# All-mpnet-base-v2 model fine-tuned for questions clustering |
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. |
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This model is named **all-mpnet-base-questions-clustering-en** since it is a Sentence Transformers model specifically fine-tuned for a questions clustering task. Three public dataset (Quora, WikiAnswer and StackExchange) has been used to enhance the model performance specifically in mapping questions with similar meanings. |
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## Usage (Sentence-Transformers) |
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Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: |
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``` |
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pip install -U sentence-transformers |
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``` |
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Then you can use the model like this: |
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```python |
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from sentence_transformers import SentenceTransformer |
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sentences = ["This is an example sentence", "Each sentence is converted"] |
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model = SentenceTransformer('aiknowyou/all-mpnet-base-questions-clustering-en') |
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embeddings = model.encode(sentences) |
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print(embeddings) |
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``` |
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## Evaluation Results |
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The present model has been evaluated by employing a test set belonging to the WikiAnswer dataset. The evaluation results are the following: |
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[ |
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{ |
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"epoch": 1, |
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"cossim_accuracy": 0.9931843415744172, |
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"cossim_accuracy_threshold": 0.35143423080444336, |
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"cossim_f1": 0.9897547191636324, |
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"cossim_precision": 0.9913437348280885, |
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"cossim_recall": 0.9881707893839572, |
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"cossim_f1_threshold": 0.35143423080444336, |
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"cossim_ap": 0.9989950013637923, |
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"manhattan_accuracy": 0.9934042015236294, |
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"manhattan_accuracy_threshold": 24.160316467285156, |
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"manhattan_f1": 0.9900818249442103, |
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"manhattan_precision": 0.9920113508380628, |
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"manhattan_recall": 0.9881597905828264, |
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"manhattan_f1_threshold": 24.160316467285156, |
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"manhattan_ap": 0.9990576126715013, |
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"euclidean_accuracy": 0.9931843415744172, |
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"euclidean_accuracy_threshold": 1.1389167308807373, |
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"euclidean_f1": 0.9897547191636324, |
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"euclidean_precision": 0.9913437348280885, |
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"euclidean_recall": 0.9881707893839572, |
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"euclidean_f1_threshold": 1.1389167308807373, |
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"euclidean_ap": 0.9989921332302106, |
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"dot_accuracy": 0.9931843415744172, |
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"dot_accuracy_threshold": 0.35143429040908813, |
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"dot_f1": 0.9897547191636324, |
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"dot_precision": 0.9913437348280885, |
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"dot_recall": 0.9881707893839572, |
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"dot_f1_threshold": 0.35143429040908813, |
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"dot_ap": 0.9989933009226604 |
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} |
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] |
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For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) |
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## Training |
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The model was trained with the parameters: |
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**DataLoader**: |
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`torch.utils.data.dataloader.DataLoader` of length 34123 with parameters: |
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``` |
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{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} |
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``` |
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**Loss**: |
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`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: |
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``` |
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{'scale': 20.0, 'similarity_fct': 'cos_sim'} |
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``` |
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**DataLoader**: |
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`torch.utils.data.dataloader.DataLoader` of length 51184 with parameters: |
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``` |
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{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} |
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``` |
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**Loss**: |
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`sentence_transformers.losses.OnlineContrastiveLoss.OnlineContrastiveLoss` |
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Parameters of the fit()-Method: |
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``` |
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{ |
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"epochs": 2, |
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"evaluation_steps": 0, |
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"evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator", |
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"max_grad_norm": 1, |
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"optimizer_class": "<class 'torch.optim.adamw.AdamW'>", |
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"optimizer_params": { |
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"lr": 2e-05 |
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}, |
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"scheduler": "WarmupLinear", |
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"steps_per_epoch": null, |
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"warmup_steps": 1000, |
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"weight_decay": 0.01 |
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} |
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``` |
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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}) |
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(2): Normalize() |
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) |
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``` |
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## Contribution |
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Thanks to [@tradicio](https://huggingface.co/tradicio) for adding this model. |
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## License |
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This work is licensed under a |
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[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa]. |
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[![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa] |
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[cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/ |
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[cc-by-nc-sa-image]: https://licensebuttons.net/l/by-nc-sa/4.0/88x31.png |
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