Update README.md

README.md

@@ -21,9 +21,13 @@ We present DistilCamemBERT-NLI which is [DistilCamemBERT](https://huggingface.co
 This modelization is close to [BaptisteDoyen/camembert-base-xnli](https://huggingface.co/BaptisteDoyen/camembert-base-xnli) based on [CamemBERT](https://huggingface.co/camembert-base) model. The problem of the modelizations based on CamemBERT is at the scaling moment, for the production phase for example. Indeed, inference cost can be a technological issue. To counteract this effect, we propose this modelization which divides the inference time by 2 with the same consumption power thanks to DistilCamemBERT.
 
 Dataset
-=======
+-------
 
-The dataset (XNLI)[https://huggingface.co/datasets/xnli] is composed of 392,702 premises with their hypothesis for the train and 5,010 couples for the test.
+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 :
+$$P(premise\in\{contradiction, entailment, neutral\}\vert hypothesis)$$
+
+Evaluation results
+------------------
 
 | **class**          | **precision (%)** | **f1-score (%)** | **support** |
 | :----------------: | :---------------: | :--------------: | :---------: |
@@ -32,6 +36,8 @@ The dataset (XNLI)[https://huggingface.co/datasets/xnli] is composed of 392,702
 | **entailment**     | 82.90             | 78.87            | 1,670       |
 | **neutral**        | 72.18             | 74.04            | 1,670       |
 
+Benchmark
+---------
 
 | **NLI**          | **time (ms)** | **MCC (x100)** |
 | :--------------: | :-----------: | :------------: |