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CrossEncoder-camembert-large

Sentence Similarity Transformers PyTorch
French camembert text-classification Text Sentence Similarity Sentence-Embedding camembert-base Eval Results Inference Endpoints
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Van Tuan DANG
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metadata
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 Cross-Encoder class.

Training Data

This model was trained on the STS benchmark dataset. 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 installed:

pip install -U sentence-transformers

Then you can use the model like this:

from sentence_transformers import CrossEncoder
model = CrossEncoder('dangvantuan/CrossEncoder-camembert-large', 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.

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

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: