Update README.md

README.md

@@ -39,13 +39,13 @@ Evaluation results
 Benchmark
 ---------
 
-We compare the [DistilCamemBERT](https://huggingface.co/cmarkea/distilcamembert-base) model with 2 other modelizations working on french language. The first one [BaptisteDoyen/camembert-base-xnli](https://huggingface.co/BaptisteDoyen/camembert-base-xnli) is based on well named [CamemBERT](https://huggingface.co/camembert-base), the french RoBERTa model and the second one [MoritzLaurer/mDeBERTa-v3-base-mnli-xnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli) based on [mDeBERTav3](https://huggingface.co/microsoft/mdeberta-v3-base) a multilingual model. To compare the performances the metric [MCC (Matthews Correlation Coefficient)](https://en.wikipedia.org/wiki/Phi_coefficient) was used and for the mean inference time measure, an **AMD Ryzen 5 4500U @ 2.3GHz with 6 cores** was used:
+We compare the [DistilCamemBERT](https://huggingface.co/cmarkea/distilcamembert-base) model with 2 other modelizations working on french language. The first one [BaptisteDoyen/camembert-base-xnli](https://huggingface.co/BaptisteDoyen/camembert-base-xnli) is based on well named [CamemBERT](https://huggingface.co/camembert-base), the french RoBERTa model and the second one [MoritzLaurer/mDeBERTa-v3-base-mnli-xnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli) based on [mDeBERTav3](https://huggingface.co/microsoft/mdeberta-v3-base) a multilingual model. To compare the performances the metrics of accuracy and [MCC (Matthews Correlation Coefficient)](https://en.wikipedia.org/wiki/Phi_coefficient) was used and for the mean inference time measure, an **AMD Ryzen 5 4500U @ 2.3GHz with 6 cores** was used:
 
-| **NLI**          | **time (ms)** | **MCC (x100)** |
-| :--------------: | :-----------: | :------------: |
-| [cmarkea/distilcamembert-base-nli](https://huggingface.co/cmarkea/distilcamembert-base-nli) | **51.35**            | 66.24     |
-| [BaptisteDoyen/camembert-base-xnli](https://huggingface.co/BaptisteDoyen/camembert-base-xnli) | 105.0              | 72.67         |
-| [MoritzLaurer/mDeBERTa-v3-base-mnli-xnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli) | 299.18 | **75.15**         |
+| **NLI**          | **time (ms)** | **accuracy (%)** | **MCC (x100)** |
+| :--------------: | :-----------: | :--------------: | :------------: |
+| [cmarkea/distilcamembert-base-nli](https://huggingface.co/cmarkea/distilcamembert-base-nli) | **51.35**            | 77.45     | 66.24         |
+| [BaptisteDoyen/camembert-base-xnli](https://huggingface.co/BaptisteDoyen/camembert-base-xnli) | 105.0              | 81.72     | 72.67         |
+| [MoritzLaurer/mDeBERTa-v3-base-mnli-xnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli) | 299.18 | **83.43** | **75.15**     |
 
 Zero-shot classification
 ------------------------
@@ -55,19 +55,19 @@ $$P(hypothesis=c|premise)=\frac{e^{P(premise=entailment\vert hypothesis\; c)}}{\
 
 For this part, we use 2 datasets, the first one: [allocine](https://huggingface.co/datasets/allocine) used to train the sentiment analysis models. The dataset 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.
 
-| **[allocine](https://huggingface.co/datasets/allocine)**     | **time (ms)** | **MCC (x100)** |
-| :--------------: | :-----------: | :------------: |
-| [cmarkea/distilcamembert-base-nli](https://huggingface.co/cmarkea/distilcamembert-base-nli) | **205.54**           | 63.71         |
-| [BaptisteDoyen/camembert-base-xnli](https://huggingface.co/BaptisteDoyen/camembert-base-xnli) | 378.39             | **73.74**     |
-| [MoritzLaurer/mDeBERTa-v3-base-mnli-xnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli) | 520.58 | 70.05         |
+| **[allocine](https://huggingface.co/datasets/allocine)**     | **time (ms)** | **accuracy (%)** | **MCC (x100)** |
+| :--------------: | :-----------: | :--------------: | :------------: |
+| [cmarkea/distilcamembert-base-nli](https://huggingface.co/cmarkea/distilcamembert-base-nli) | **205.54**           | 80.59         | 63.71         |
+| [BaptisteDoyen/camembert-base-xnli](https://huggingface.co/BaptisteDoyen/camembert-base-xnli) | 378.39             | **86.37**     | **73.74**     |
+| [MoritzLaurer/mDeBERTa-v3-base-mnli-xnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli) | 520.58 | 84.97         | 70.05         |
 
 The second one: [mlsum](https://huggingface.co/datasets/mlsum) used to train the summarization models. We use the articles summary part to predict their topics. In this aim, we aggregate sub-topics and select a few of them. In this case, the hypothesis template used is "C'est un article traitant de {}." and the candidate labels are: "économie", "politique", "sport", "cinéma", "musique" and "science".
 
-| **[mlsum](https://huggingface.co/datasets/mlsum)**        | **time (ms)** | **MCC (x100)** |
-| :--------------: | :-----------: | :------------: |
-| [cmarkea/distilcamembert-base-nli](https://huggingface.co/cmarkea/distilcamembert-base-nli) | **261.99**           | 60.12         |
-| [BaptisteDoyen/camembert-base-xnli](https://huggingface.co/BaptisteDoyen/camembert-base-xnli) | 499.45             | **60.14**     |
-| [MoritzLaurer/mDeBERTa-v3-base-mnli-xnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli) | 591.34 | 56.06         |
+| **[mlsum](https://huggingface.co/datasets/mlsum)**        | **time (ms)** |  **accuracy (%)** | **MCC (x100)** |
+| :--------------: | :-----------: | :--------------: | :------------: |
+| [cmarkea/distilcamembert-base-nli](https://huggingface.co/cmarkea/distilcamembert-base-nli) | **261.99**           |               | 60.12         |
+| [BaptisteDoyen/camembert-base-xnli](https://huggingface.co/BaptisteDoyen/camembert-base-xnli) | 499.45             |               | **60.14**     |
+| [MoritzLaurer/mDeBERTa-v3-base-mnli-xnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli) | 591.34 |               | 56.06         |
 
 How to use DistilCamemBERT-Sentiment
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