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README.md
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The dataset XNLI from [FLUE](https://huggingface.co/datasets/flue) is composed of 392,702 premises with their hypothesis for the train and 5,010 couples for the test. The goal is to predict textual entailment (does sentence A imply/contradict/neither sentence B?) and is a classification task (given two sentences, predict one of three labels). The sentence A is called *premise* and sentence B is called *hypothesis*, then the goal of modelization is determined as follows:
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$$P(premise\in\{contradiction, entailment, neutral\}\vert hypothesis)$$
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Evaluation results
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| [BaptisteDoyen/camembert-base-xnli](https://huggingface.co/BaptisteDoyen/camembert-base-xnli) | 499.45 | | **60.14** |
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| [MoritzLaurer/mDeBERTa-v3-base-mnli-xnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli) | 591.34 | | 56.06 |
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How to use DistilCamemBERT-
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```python
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from transformers import pipeline
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classifier = pipeline(
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-------
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The dataset XNLI from [FLUE](https://huggingface.co/datasets/flue) is composed of 392,702 premises with their hypothesis for the train and 5,010 couples for the test. The goal is to predict textual entailment (does sentence A imply/contradict/neither sentence B?) and is a classification task (given two sentences, predict one of three labels). The sentence A is called *premise* and sentence B is called *hypothesis*, then the goal of modelization is determined as follows:
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$$P(premise=c\in\{contradiction, entailment, neutral\}\vert hypothesis)$$
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Evaluation results
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| [BaptisteDoyen/camembert-base-xnli](https://huggingface.co/BaptisteDoyen/camembert-base-xnli) | 499.45 | | **60.14** |
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| [MoritzLaurer/mDeBERTa-v3-base-mnli-xnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli) | 591.34 | | 56.06 |
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How to use DistilCamemBERT-NLI
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------------------------------
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```python
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from transformers import pipeline
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classifier = pipeline(
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