add discription

README.md

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-Pre-trained sentence embedding models are the state-of-the-art of Sentence Embeddings for French:
+---
+language: fr
+datasets:
+- stsb_multi_mt
+tags:
+- Text
+- Text 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
+---
+
+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-large](https://huggingface.co/camembert/camembert-large).
+[Using Siamese BERT-Networks with 'sentences-transformers'](https://www.sbert.net/) and dataset [stsb](https://huggingface.co/datasets/stsb_multi_mt)
+
+
+## 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
+dev_samples = convert_dataset(df_dev)
+val_evaluator = EmbeddingSimilarityEvaluator.from_input_examples(dev_samples, name='sts-dev')
+val_evaluator(model, output_path="./")
+
+test_samples = convert_dataset(df_dev)
+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  |
+| ------------- | ------------- |
+| [dangvantuan/sentence-camembert-large](https://huggingface.co/camembert/camembert-large)| 88.2 |88.02 |
+| [distiluse-base-multilingual-cased-v1](https://www.sbert.net/examples/training/multilingual/README.html) | 81.15 | 81.15|
+- On test
+| Model  | Pearson correlation | Spearman correlation  |
+| ------------- | ------------- |
+| [dangvantuan/sentence-camembert-large](https://huggingface.co/camembert/camembert-large)| 85.9 | 85.8|
+| [distiluse-base-multilingual-cased-v1](https://www.sbert.net/examples/training/multilingual/README.html) | 79.16 | 77.73|
+
+
+## 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}
+	}
+
+@inproceedings{martin2020camembert,
+  title={CamemBERT: a Tasty French Language Model},
+  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},
+  booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
+  year={2020}
+}