Push the entire model

.gitattributes

@@ -30,3 +30,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+pytorch_model.bin filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

@@ -0,0 +1,7 @@
+{
+  "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

@@ -1,3 +1,151 @@
 ---
+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

@@ -0,0 +1,2 @@
+epoch,steps,cossim_accuracy,cossim_accuracy_threshold,cossim_f1,cossim_precision,cossim_recall,cossim_f1_threshold,cossim_ap,manhattan_accuracy,manhattan_accuracy_threshold,manhattan_f1,manhattan_precision,manhattan_recall,manhattan_f1_threshold,manhattan_ap,euclidean_accuracy,euclidean_accuracy_threshold,euclidean_f1,euclidean_precision,euclidean_recall,euclidean_f1_threshold,euclidean_ap,dot_accuracy,dot_accuracy_threshold,dot_f1,dot_precision,dot_recall,dot_f1_threshold,dot_ap
+0,0,0.9893422889619312,0.3684113025665283,0.9839550952295576,0.9870342598792521,0.9808950824360144,0.3684113025665283,0.9981098929310646,0.9887431706003276,24.35479736328125,0.9830630888912166,0.9855571523325226,0.9805816166037902,24.358116149902344,0.9979432632969408,0.9893422889619312,1.123911738395691,0.9839550952295576,0.9870342598792521,0.9808950824360144,1.123911738395691,0.9981070245867787,0.9893422889619312,0.36841127276420593,0.9839550952295576,0.9870342598792521,0.9808950824360144,0.36841127276420593,0.9981101239841529

config.json

@@ -0,0 +1,24 @@
+{
+  "_name_or_path": "/home/utente/.cache/torch/sentence_transformers/sentence-transformers_all-mpnet-base-v2/",
+  "architectures": [
+    "MPNetModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "mpnet",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "relative_attention_num_buckets": 32,
+  "torch_dtype": "float32",
+  "transformers_version": "4.22.0.dev0",
+  "vocab_size": 30527
+}

config_sentence_transformers.json

@@ -0,0 +1,7 @@
+{
+  "__version__": {
+    "sentence_transformers": "2.0.0",
+    "transformers": "4.6.1",
+    "pytorch": "1.8.1"
+  }
+}

eval/binary_classification_evaluation_results.csv

@@ -0,0 +1,3 @@
+epoch,steps,cossim_accuracy,cossim_accuracy_threshold,cossim_f1,cossim_precision,cossim_recall,cossim_f1_threshold,cossim_ap,manhattan_accuracy,manhattan_accuracy_threshold,manhattan_f1,manhattan_precision,manhattan_recall,manhattan_f1_threshold,manhattan_ap,euclidean_accuracy,euclidean_accuracy_threshold,euclidean_f1,euclidean_precision,euclidean_recall,euclidean_f1_threshold,euclidean_ap,dot_accuracy,dot_accuracy_threshold,dot_f1,dot_precision,dot_recall,dot_f1_threshold,dot_ap
+0,-1,0.9906596164909619,0.3930458724498749,0.9859670564400695,0.9870212179663819,0.9849151442492768,0.3930458724498749,0.9984901891426392,0.991234916691401,23.361751556396484,0.9868355558244485,0.9869576936405692,0.9867134482341425,23.616687774658203,0.9986105475645636,0.9906596164909619,1.1017749309539795,0.9859670564400695,0.9870212179663819,0.9849151442492768,1.1017749309539795,0.9984873210075305,0.9906596164909619,0.39304593205451965,0.9859670564400695,0.9870212179663819,0.9849151442492768,0.39304593205451965,0.9984897548380124
+1,-1,0.9931843415744172,0.35143423080444336,0.9897547191636324,0.9913437348280885,0.9881707893839572,0.35143423080444336,0.9989950013637923,0.9934042015236294,24.160316467285156,0.9900818249442103,0.9920113508380628,0.9881597905828264,24.160316467285156,0.9990576126715013,0.9931843415744172,1.1389167308807373,0.9897547191636324,0.9913437348280885,0.9881707893839572,1.1389167308807373,0.9989921332302106,0.9931843415744172,0.35143429040908813,0.9897547191636324,0.9913437348280885,0.9881707893839572,0.35143429040908813,0.9989933009226604

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9b0627657a0954b301f54383025898a0de1dc0c76d37a852cb2195167f66926c
+size 438014769

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 384,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,15 @@
+{
+  "bos_token": "<s>",
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

@@ -0,0 +1,16 @@
+{
+  "bos_token": "<s>",
+  "cls_token": "<s>",
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "name_or_path": "/home/utente/.cache/torch/sentence_transformers/sentence-transformers_all-mpnet-base-v2/",
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "special_tokens_map_file": null,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "MPNetTokenizer",
+  "unk_token": "[UNK]"
+}