Update README.md

README.md

@@ -53,15 +53,17 @@ Zero-shot classification
 The main advantage of such modelization is to create a zero-shot classifier allowing text classification without training. This task can be summarized by:
 $$P(hypothesis=c|premise)=\frac{e^{P(premise=entailment\vert hypothesis\; c)}}{\sum_{i\in\mathcal{C}}e^{P(premise=entailment\vert hypothesis\; i)}}$$
 
-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.
+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.
 
-| **Allociné**     | **time (ms)** | **MCC (x100)** |
+| **[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         |
 
-| **MLSum**        | **time (ms)** | **MCC (x100)** |
+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", "technologie" 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**     |