init

README.md

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 ---
-license: mit
+pipeline_tag: sentence-similarity
+language: fr
+datasets:
+- stsb_multi_mt
+tags:
+- Text
+- Sentence Similarity
+- Sentence-Embedding
+- camembert-large
+license: apache-2.0
+model-index:
+- name: sentence-camembert-large 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
 ---
+## Description:
+[**Sentence-CamemBERT-Large**](https://huggingface.co/dangvantuan/sentence-camembert-large) is the Embedding Model for French developed by [La Javaness](https://www.lajavaness.com/). The purpose of this embedding model is to represent the content and semantics of a French sentence in a mathematical vector which allows it to understand the meaning of the text-beyond individual words in queries and documents, offering a powerful semantic search.
+## Pre-trained sentence embedding models are state-of-the-art of Sentence Embeddings for French.
+The model is Fine-tuned using pre-trained [facebook/camembert-large](https://huggingface.co/camembert/camembert-large) 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-large")
+
+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 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-large](https://huggingface.co/dangvantuan/sentence-camembert-large)| 88.2 |88.02 | 336M| 
+| [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 |
+| [GPT-3 (text-davinci-003)](https://platform.openai.com/docs/models) | 85 | NaN|175B |
+| [GPT-(text-embedding-ada-002)](https://platform.openai.com/docs/models) | 79.75 | 80.44|NaN |
+- On test
+
+
+| Model  | Pearson correlation | Spearman correlation  | 
+| ------------- | ------------- | ------------- |
+| [dangvantuan/sentence-camembert-large](https://huggingface.co/dangvantuan/sentence-camembert-large)| 85.9 | 85.8|
+| [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|
+| [GPT-3 (text-davinci-003)](https://platform.openai.com/docs/models) | 82 | NaN|175B |
+| [GPT-(text-embedding-ada-002)](https://platform.openai.com/docs/models) | 79.05 | 77.56|NaN |
+
+
+## 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": "camembert/camembert-large",
+  "architectures": [
+    "CamembertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "camembert",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.3.3",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 32005
+}

model.safetensors

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pytorch_model.bin

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sentence_bert_config.json

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+{
+  "max_seq_length": 128
+}

sentencepiece.bpe.model

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special_tokens_map.json

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tf_model.h5

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tokenizer_config.json

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