Save model files

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,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

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+---
+pipeline_tag: sentence-similarity
+language: fr
+datasets:
+- stsb_multi_mt
+tags:
+- Text
+- Sentence Similarity
+- Sentence-Embedding
+- camembert-base
+license: apache-2.0
+model-index:
+- name: sentence-camembert-base by Van Tuan DANG
+  results:
+  - task:
+      name: Sentence-Embedding
+      type: Text Similarity
+    dataset:
+      name: Text Similarity fr
+      type: stsb_multi_mt
+      args: fr
+    metrics:
+    - name: Test Pearson correlation coefficient
+      type: Pearson_correlation_coefficient
+      value: xx.xx
+library_name: sentence-transformers
+---
+
+## Pre-trained sentence embedding models are the state-of-the-art of Sentence Embeddings for French.
+Model is Fine-tuned using pre-trained [facebook/camembert-base](https://huggingface.co/camembert/camembert-base) and
+[Siamese BERT-Networks with 'sentences-transformers'](https://www.sbert.net/) on dataset [stsb](https://huggingface.co/datasets/stsb_multi_mt/viewer/fr/train)
+
+
+## Usage
+The model can be used directly (without a language model) as follows:
+
+```python
+from sentence_transformers import SentenceTransformer
+model =  SentenceTransformer("dangvantuan/sentence-camembert-base")
+
+sentences = ["Un avion est en train de décoller.",
+          "Un homme joue d'une grande flûte.",
+          "Un homme étale du fromage râpé sur une pizza.",
+          "Une personne jette un chat au plafond.",
+          "Une personne est en train de plier un morceau de papier.",
+          ]
+
+embeddings = model.encode(sentences)
+```
+
+## Evaluation
+The model can be evaluated as follows on the French test data of stsb.
+
+```python
+from sentence_transformers import SentenceTransformer
+from sentence_transformers.readers import InputExample
+from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
+from datasets import load_dataset
+def convert_dataset(dataset):
+    dataset_samples=[]
+    for df in dataset:
+        score = float(df['similarity_score'])/5.0  # Normalize score to range 0 ... 1
+        inp_example = InputExample(texts=[df['sentence1'], 
+                                    df['sentence2']], label=score)
+        dataset_samples.append(inp_example)
+    return dataset_samples
+
+# Loading the dataset for evaluation
+df_dev = load_dataset("stsb_multi_mt", name="fr", split="dev")
+df_test = load_dataset("stsb_multi_mt", name="fr", split="test")
+
+# Convert the dataset for evaluation
+
+# For Dev set:
+dev_samples = convert_dataset(df_dev)
+val_evaluator = EmbeddingSimilarityEvaluator.from_input_examples(dev_samples, name='sts-dev')
+val_evaluator(model, output_path="./")
+
+# For Test set:
+test_samples = convert_dataset(df_test)
+test_evaluator = EmbeddingSimilarityEvaluator.from_input_examples(test_samples, name='sts-test')
+test_evaluator(model, output_path="./")
+```
+
+**Test Result**: 
+The performance is measured using Pearson and Spearman correlation:
+- On dev
+
+
+| Model  | Pearson correlation | Spearman correlation  |  #params  |
+| ------------- | ------------- | ------------- |------------- |
+| [dangvantuan/sentence-camembert-base](https://huggingface.co/dangvantuan/sentence-camembert-base)| 86.73 |86.54 | 110M |
+| [distiluse-base-multilingual-cased](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased) | 79.22 | 79.16|135M |
+- On test
+
+
+| Model  | Pearson correlation | Spearman correlation  |
+| ------------- | ------------- | ------------- |
+| [dangvantuan/sentence-camembert-base](https://huggingface.co/dangvantuan/sentence-camembert-base)| 82.36 | 81.64|
+| [distiluse-base-multilingual-cased](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased) | 78.62 | 77.48|
+
+
+## Citation
+
+
+	@article{reimers2019sentence,
+	   title={Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks},
+	   author={Nils Reimers, Iryna Gurevych},
+	   journal={https://arxiv.org/abs/1908.10084},
+	   year={2019}
+	}
+
+
+	@article{martin2020camembert,
+	   title={CamemBERT: a Tasty French Language Mode},
+	   author={Martin, Louis and Muller, Benjamin and Su{\'a}rez, Pedro Javier Ortiz and Dupont, Yoann and Romary, Laurent and de la Clergerie, {\'E}ric Villemonte and Seddah, Djam{\'e} and Sagot, Beno{\^\i}t},
+	   journal={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
+	   year={2020}
+	}

config.json

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+{
+  "_name_or_path": "dangvantuan/sentence-camembert-base",
+  "architectures": [
+    "CamembertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "eos_token_ids": 0,
+  "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": "camembert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 32005
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "2.1.0",
+    "transformers": "4.11.3",
+    "pytorch": "1.9.1+cu102"
+  },
+  "prompts": {},
+  "default_prompt_name": null
+}

model.safetensors

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+size 442510176

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

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+{
+  "max_seq_length": 128,
+  "do_lower_case": false
+}

sentencepiece.bpe.model

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+oid sha256:988bc5a00281c6d210a5d34bd143d0363741a432fefe741bf71e61b1869d4314
+size 810912

special_tokens_map.json

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+{
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+    "<s>NOTUSED",
+    "</s>NOTUSED"
+  ],
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+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
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+      "content": "<s>NOTUSED",
+      "lstrip": false,
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+    "<s>NOTUSED",
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+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "max_length": 128,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "sp_model_kwargs": {},
+  "stride": 0,
+  "tokenizer_class": "CamembertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "<unk>"
+}