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--- |
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pipeline_tag: sentence-similarity |
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language: fr |
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datasets: |
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- stsb_multi_mt |
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tags: |
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- Text |
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- Sentence Similarity |
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- Sentence-Embedding |
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- camembert-base |
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license: apache-2.0 |
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model-index: |
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- name: sentence-camembert-base by Van Tuan DANG |
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results: |
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- task: |
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name: Sentence-Embedding |
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type: Text Similarity |
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dataset: |
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name: Text Similarity fr |
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type: stsb_multi_mt |
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args: fr |
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metrics: |
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- name: Test Pearson correlation coefficient |
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type: Pearson_correlation_coefficient |
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value: xx.xx |
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--- |
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## Model |
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Cross-Encoder for sentence-similarity |
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This model was trained using [sentence-transformers](https://www.SBERT.net) Cross-Encoder class. |
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## Training Data |
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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. |
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## Usage (Sentence-Transformers) |
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Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: |
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``` |
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pip install -U sentence-transformers |
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``` |
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Then you can use the model like this: |
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```python |
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from sentence_transformers import CrossEncoder |
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model = CrossEncoder('dangvantuan/CrossEncoder-camembert-large', max_length=128) |
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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") ]) |
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``` |
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## Evaluation |
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The model can be evaluated as follows on the French test data of stsb. |
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```python |
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from sentence_transformers.readers import InputExample |
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from sentence_transformers.cross_encoder.evaluation import CECorrelationEvaluator |
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from datasets import load_dataset |
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def convert_dataset(dataset): |
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dataset_samples=[] |
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for df in dataset: |
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score = float(df['similarity_score'])/5.0 # Normalize score to range 0 ... 1 |
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inp_example = InputExample(texts=[df['sentence1'], |
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df['sentence2']], label=score) |
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dataset_samples.append(inp_example) |
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return dataset_samples |
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# Loading the dataset for evaluation |
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df_dev = load_dataset("stsb_multi_mt", name="fr", split="dev") |
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df_test = load_dataset("stsb_multi_mt", name="fr", split="test") |
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# Convert the dataset for evaluation |
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# For Dev set: |
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dev_samples = convert_dataset(df_dev) |
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val_evaluator = CECorrelationEvaluator.from_input_examples(dev_samples, name='sts-dev') |
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val_evaluator(model, output_path="./") |
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# For Test set |
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test_samples = convert_dataset(df_test) |
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test_evaluator = CECorrelationEvaluator.from_input_examples(test_samples, name='sts-test') |
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test_evaluator(models, output_path="./") |
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``` |
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**Test Result**: |
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The performance is measured using Pearson and Spearman correlation: |
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- On dev |
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| Model | Pearson correlation | Spearman correlation | #params | |
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| ------------- | ------------- | ------------- |------------- | |
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| [dangvantuan/CrossEncoder-camembert-large](https://huggingface.co/dangvantuan/CrossEncoder-camembert-large)| 90.11 |90.01 | 336M | |
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- On test |
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| Model | Pearson correlation | Spearman correlation | |
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| ------------- | ------------- | ------------- | |
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| [dangvantuan/CrossEncoder-camembert-large](https://huggingface.co/dangvantuan/CrossEncoder-camembert-large)| 88.16 | 87.57| |
