## 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}
	}
{"language": "fr", "license": "apache-2.0", "tags": ["Text", "Sentence Similarity", "Sentence-Embedding", "camembert-large"], "datasets": ["stsb_multi_mt"], "pipeline_tag": "sentence-similarity", "model-index": [{"name": "sentence-camembert-large by Van Tuan DANG", "results": [{"task": {"type": "Text Similarity", "name": "Sentence-Embedding"}, "dataset": {"name": "Text Similarity fr", "type": "stsb_multi_mt", "args": "fr"}, "metrics": [{"type": "Pearson_correlation_coefficient", "value": "xx.xx", "name": "Test Pearson correlation coefficient"}]}]}]}
transformers pytorch tf safetensors camembert feature-extraction Text Sentence Similarity Sentence-Embedding camembert-large sentence-similarity fr dataset:stsb_multi_mt arxiv:1908.10084 license:apache-2.0 model-index endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tf #safetensors #camembert #feature-extraction #Text #Sentence Similarity #Sentence-Embedding #camembert-large #sentence-similarity #fr #dataset-stsb_multi_mt #arxiv-1908.10084 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 

Description:
------------


Sentence-CamemBERT-Large is the Embedding Model for French developed by La Javaness. 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 and
Siamese BERT-Networks with 'sentences-transformers' on dataset stsb


Usage
-----


The model can be used directly (without a language model) as follows:


Evaluation
----------


The model can be evaluated as follows on the French test data of stsb.


Test Result:
The performance is measured using Pearson and Spearman correlation:


* On dev



* On test


Model: dangvantuan/sentence-camembert-large, Pearson correlation: 85.9, Spearman correlation: 85.8
Model: dangvantuan/sentence-camembert-base, Pearson correlation: 82.36, Spearman correlation: 81.64
Model: distiluse-base-multilingual-cased, Pearson correlation: 78.62, Spearman correlation: 77.48
Model: GPT-3 (text-davinci-003), Pearson correlation: 82, Spearman correlation: NaN
Model: GPT-(text-embedding-ada-002), Pearson correlation: 79.05, Spearman correlation: 77.56


@article{reimers2019sentence,
title={Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks},
author={Nils Reimers, Iryna Gurevych},
journal={URL
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}
}

```


TAGS
#transformers #pytorch #tf #safetensors #camembert #feature-extraction #Text #Sentence Similarity #Sentence-Embedding #camembert-large #sentence-similarity #fr #dataset-stsb_multi_mt #arxiv-1908.10084 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 

passage: TAGS
#transformers #pytorch #tf #safetensors #camembert #feature-extraction #Text #Sentence Similarity #Sentence-Embedding #camembert-large #sentence-similarity #fr #dataset-stsb_multi_mt #arxiv-1908.10084 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 
