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Model: aiknowyou/all-mpnet-base-questions-clustering-en
Source: Original Platform

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1_Pooling/config.json

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+{
+  "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
+}

README.md

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+---
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+language: en
+datasets:
+- quora
+- embedding-data/WikiAnswers
+- flax-sentence-embeddings/stackexchange_xml
+license: cc-by-nc-sa-4.0
+---
+
+# All-mpnet-base-v2 model fine-tuned for questions clustering 
+
+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.
+
+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.    
+
+## Usage (Sentence-Transformers)
+
+Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+
+```
+pip install -U sentence-transformers
+```
+
+Then you can use the model like this:
+
+```python
+from sentence_transformers import SentenceTransformer
+sentences = ["This is an example sentence", "Each sentence is converted"]
+
+model = SentenceTransformer('aiknowyou/all-mpnet-base-questions-clustering-en')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+
+
+## Evaluation Results
+
+The present model has been evaluated by employing a test set belonging to the WikiAnswer dataset. The evaluation results are the following:
+
+[
+  {
+    "epoch": 1,
+    "cossim_accuracy": 0.9931843415744172,
+    "cossim_accuracy_threshold": 0.35143423080444336,
+    "cossim_f1": 0.9897547191636324,
+    "cossim_precision": 0.9913437348280885,
+    "cossim_recall": 0.9881707893839572,
+    "cossim_f1_threshold": 0.35143423080444336,
+    "cossim_ap": 0.9989950013637923,
+    "manhattan_accuracy": 0.9934042015236294,
+    "manhattan_accuracy_threshold": 24.160316467285156,
+    "manhattan_f1": 0.9900818249442103,
+    "manhattan_precision": 0.9920113508380628,
+    "manhattan_recall": 0.9881597905828264,
+    "manhattan_f1_threshold": 24.160316467285156,
+    "manhattan_ap": 0.9990576126715013,
+    "euclidean_accuracy": 0.9931843415744172,
+    "euclidean_accuracy_threshold": 1.1389167308807373,
+    "euclidean_f1": 0.9897547191636324,
+    "euclidean_precision": 0.9913437348280885,
+    "euclidean_recall": 0.9881707893839572,
+    "euclidean_f1_threshold": 1.1389167308807373,
+    "euclidean_ap": 0.9989921332302106,
+    "dot_accuracy": 0.9931843415744172,
+    "dot_accuracy_threshold": 0.35143429040908813,
+    "dot_f1": 0.9897547191636324,
+    "dot_precision": 0.9913437348280885,
+    "dot_recall": 0.9881707893839572,
+    "dot_f1_threshold": 0.35143429040908813,
+    "dot_ap": 0.9989933009226604
+  }
+]
+
+For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
+
+
+## Training
+The model was trained with the parameters:
+
+**DataLoader**:
+
+`torch.utils.data.dataloader.DataLoader` of length 34123 with parameters:
+```
+{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
+```
+
+**Loss**:
+
+`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
+  ```
+  {'scale': 20.0, 'similarity_fct': 'cos_sim'}
+  ```
+
+**DataLoader**:
+
+`torch.utils.data.dataloader.DataLoader` of length 51184 with parameters:
+```
+{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
+```
+
+**Loss**:
+
+`sentence_transformers.losses.OnlineContrastiveLoss.OnlineContrastiveLoss` 
+
+Parameters of the fit()-Method:
+```
+{
+    "epochs": 2,
+    "evaluation_steps": 0,
+    "evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
+    "max_grad_norm": 1,
+    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
+    "optimizer_params": {
+        "lr": 2e-05
+    },
+    "scheduler": "WarmupLinear",
+    "steps_per_epoch": null,
+    "warmup_steps": 1000,
+    "weight_decay": 0.01
+}
+```
+
+
+## Full Model Architecture
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel 
+  (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})
+  (2): Normalize()
+)
+```
+
+## Contribution
+
+Thanks to [@tradicio](https://huggingface.co/tradicio) for adding this model.
+
+## License
+
+This work is licensed under a
+[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa].
+
+[![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa]
+
+[cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/
+[cc-by-nc-sa-image]: https://licensebuttons.net/l/by-nc-sa/4.0/88x31.png

binary_classification_evaluation_results.csv

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config.json

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+  "architectures": [
+    "MPNetModel"
+  ],
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+  "model_type": "mpnet",
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+}

config_sentence_transformers.json

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+  "__version__": {
+    "sentence_transformers": "2.0.0",
+    "transformers": "4.6.1",
+    "pytorch": "1.8.1"
+  }
+}

eval/binary_classification_evaluation_results.csv

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modules.json

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sentence_bert_config.json

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