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README.md
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@@ -43,24 +43,29 @@ We compare the [DistilCamemBERT](https://huggingface.co/cmarkea/distilcamembert-
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| **NLI** | **time (ms)** | **MCC (x100)** |
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| :--------------: | :-----------: | :------------: |
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| [cmarkea/distilcamembert-base-nli](https://huggingface.co/cmarkea/distilcamembert-base-nli) | **51.35** |
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| [BaptisteDoyen/camembert-base-xnli](https://huggingface.co/BaptisteDoyen/camembert-base-xnli) | 105.0 | 72.67 |
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| [MoritzLaurer/mDeBERTa-v3-base-mnli-xnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli) | 299.18 | 75.15 |
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The main advantage of such modelization is to create a zero-shot classifier allowing text classification without training. This task can be summarized by:
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$$P(hypothesis=c|premise)=\frac{e^{P(premise=entailment\vert hypothesis\; c)}}{\sum_{i\in\mathcal{C}}e^{P(premise=entailment\vert hypothesis\; i)}}$$
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| **Allociné** | **time (ms)** | **MCC (x100)** |
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| :--------------: | :-----------: | :------------: |
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| [cmarkea/distilcamembert-base-nli](https://huggingface.co/cmarkea/distilcamembert-base-nli) | **205.54** |
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| [BaptisteDoyen/camembert-base-xnli](https://huggingface.co/BaptisteDoyen/camembert-base-xnli) | 378.39 | 73.74
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| [MoritzLaurer/mDeBERTa-v3-base-mnli-xnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli) | 520.58 | 70.05 |
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| **MLSum** | **time (ms)** | **MCC (x100)** |
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| :--------------: | :-----------: | :------------: |
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| [cmarkea/distilcamembert-base-nli](https://huggingface.co/cmarkea/distilcamembert-base-nli) | **261.99** |
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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-Sentiment
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| **NLI** | **time (ms)** | **MCC (x100)** |
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| :--------------: | :-----------: | :------------: |
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| [cmarkea/distilcamembert-base-nli](https://huggingface.co/cmarkea/distilcamembert-base-nli) | **51.35** | 66.24 |
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| [BaptisteDoyen/camembert-base-xnli](https://huggingface.co/BaptisteDoyen/camembert-base-xnli) | 105.0 | 72.67 |
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| [MoritzLaurer/mDeBERTa-v3-base-mnli-xnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli) | 299.18 | **75.15** |
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Zero-shot classification
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The main advantage of such modelization is to create a zero-shot classifier allowing text classification without training. This task can be summarized by:
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$$P(hypothesis=c|premise)=\frac{e^{P(premise=entailment\vert hypothesis\; c)}}{\sum_{i\in\mathcal{C}}e^{P(premise=entailment\vert hypothesis\; i)}}$$
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For this part, we use 2 datasets, the first one: [allocine](https://huggingface.co/datasets/allocine) used for training the sentiment analysis models. Is composed of 2 classes: "positif" and "négatif" appreciation of movies reviews. Here we use "Ce commentaire est {}." as the hypothesis template and "positif" and "négatif" as candidate labels.
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| **Allociné** | **time (ms)** | **MCC (x100)** |
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| :--------------: | :-----------: | :------------: |
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| [cmarkea/distilcamembert-base-nli](https://huggingface.co/cmarkea/distilcamembert-base-nli) | **205.54** | 63.71 |
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| [BaptisteDoyen/camembert-base-xnli](https://huggingface.co/BaptisteDoyen/camembert-base-xnli) | 378.39 | **73.74** |
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| [MoritzLaurer/mDeBERTa-v3-base-mnli-xnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli) | 520.58 | 70.05 |
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| **MLSum** | **time (ms)** | **MCC (x100)** |
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| :--------------: | :-----------: | :------------: |
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| [cmarkea/distilcamembert-base-nli](https://huggingface.co/cmarkea/distilcamembert-base-nli) | **261.99** | 60.12 |
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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-Sentiment
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