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README.md

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----
-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}
-	}