---
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: CrossEncoder-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:  90.34
---

## Model

Cross-Encoder Model for sentence-similarity

This model was is an improvement over the [dangvantuan/CrossEncoder-camembert-large](https://huggingface.co/dangvantuan/CrossEncoder-camembert-large) offering greater robustness and better performance

## Training Data
This model was trained on the [STS benchmark dataset](https://huggingface.co/datasets/stsb_multi_mt/viewer/fr/train) and has been combined with [Augmented SBERT](https://aclanthology.org/2021.naacl-main.28.pdf). The model benefits from Pair Sampling Strategies using two models: [CrossEncoder-camembert-large](https://huggingface.co/dangvantuan/CrossEncoder-camembert-large) and [dangvantuan/sentence-camembert-large](https://huggingface.co/dangvantuan/sentence-camembert-large). 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('Lajavaness/CrossEncoder-camembert-large', max_length=512)
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.readers import InputExample
from sentence_transformers.cross_encoder.evaluation import CECorrelationEvaluator
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, the Pearson and Spearman correlation are evaluated on many different benchmark datasets:

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  |
| ------------- | ------------- | ------------- |------------- |
| [Lajavaness/CrossEncoder-camembert-large](https://huggingface.co/dangvantuan/CrossEncoder-camembert-large)| 90.34 |90.15 | 336M |
| [dangvantuan/CrossEncoder-camembert-large](https://huggingface.co/dangvantuan/CrossEncoder-camembert-large)| 90.11 |90.01 | 336M |

- On test:

  
**Pearson score**

| Model                                  | STS-B  | STS12-fr | STS13-fr | STS14-fr | STS15-fr | STS16-fr | SICK-fr |
|---------------------------------------|--------|----------|----------|----------|----------|----------|---------|
| [Lajavaness/CrossEncoder-camembert-large](https://huggingface.co/dangvantuan/CrossEncoder-camembert-large)       | 88.63 | 90.76   | 88.24   | 90.22   | 92.23   | 82.31   | 84.61  | 
| [dangvantuan/CrossEncoder-camembert-large](https://huggingface.co/dangvantuan/CrossEncoder-camembert-large)      | 88.16 | 90.12   | 88.36   | 89.86   | 92.04   | 82.01   | 84.23  | 

**Spearman score**


| Model                                  | STS-B  | STS12-fr | STS13-fr | STS14-fr | STS15-fr | STS16-fr | SICK-fr |
|---------------------------------------|--------|----------|----------|----------|----------|----------|---------|
| [Lajavaness/CrossEncoder-camembert-large](https://huggingface.co/dangvantuan/CrossEncoder-camembert-large)      | 88.03 | 84.87   | 87.88   | 89.10   | 92.16   | 82.50   | 80.78  |
| [dangvantuan/CrossEncoder-camembert-large](https://huggingface.co/dangvantuan/CrossEncoder-camembert-large)     | 87.57 | 84.24   | 88.01   | 88.62   | 91.99   | 82.16   | 80.38  |