feat: Add Readmel

README.md

@@ -0,0 +1,97 @@
+---
+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
+---
+
+## {Model}
+
+Cross-Encoder for sentence-similarity
+
+This model was trained using [sentence-transformers](https://www.SBERT.net) Cross-Encoder class.
+
+## Training Data
+This model was trained on the [STS benchmark dataset](https://huggingface.co/datasets/stsb_multi_mt/viewer/fr/train). The model will predict a score between 0 and 1 how for the semantic similarity of two sentences.
+
+
+## Usage (Sentence-Transformers)
+
+Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+
+```
+pip install -U sentence-transformers
+```
+
+Then you can use the model like this:
+
+```python
+from sentence_transformers import CrossEncoder
+model = CrossEncoder('model_name', max_length=128)
+scores = model.predict([('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") ])
+
+```
+## 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 = CECorrelationEvaluator.from_input_examples(dev_samples, name='sts-dev')
+val_evaluator(model, output_path="./")
+
+# For Test set
+
+test_samples = convert_dataset(df_test)
+test_evaluator = CECorrelationEvaluator.from_input_examples(test_samples, name='sts-test')
+test_evaluator(models, output_path="./")
+```
+**Test Result**: 
+The performance is measured using Pearson and Spearman correlation:
+- On dev
+| Model  | Pearson correlation | Spearman correlation  |  #params  |
+| ------------- | ------------- | ------------- |------------- |
+| [dangvantuan/CrossEncoder-camembert-large](https://huggingface.co/dangvantuan/CrossEncoder-camembert-large)| 90.11 |90.01 | 336M |
+
+- On test
+| Model  | Pearson correlation | Spearman correlation  |
+| ------------- | ------------- | ------------- |
+| [dangvantuan/CrossEncoder-camembert-large](https://huggingface.co/dangvantuan/CrossEncoder-camembert-large)| 88.16 | 87.57|