Update README.md

README.md

@@ -4,19 +4,29 @@ language:
 license:
 - cc-by-nc-4.0
 size_categories:
-- 10K<n<100K
+- 1M<n<10M
 task_categories:
 - text-classification
 tags:
 - textual-entailment
+- DFP
+- french prompts
+annotations_creators:
+- found
+language_creators:
+- found
+multilinguality:
+- monolingual
+source_datasets:
+- xnli
 ---
 
 # xnli_fr_prompt_textual_entailment
 ## Summary
 
-**xnli_fr_prompt_textual_entailment** is a subset of the [**Dataset of French Prompts (DFP)**]().
-It contains **X** rows that can be used for a textual entailment task.
-The original data (without prompts) comes from the dataset [xnli](https://huggingface.co/datasets/xnli) by Conneau et al. where only the French part has been kept.
+**xnli_fr_prompt_textual_entailment** is a subset of the [**Dataset of French Prompts (DFP)**](https://huggingface.co/datasets/CATIE-AQ/DFP).  
+It contains **8,804,444** rows that can be used for a textual entailment task.  
+The original data (without prompts) comes from the dataset [xnli](https://huggingface.co/datasets/xnli) by Conneau et al. where only the French part has been kept.  
 A list of prompts (see below) was then applied in order to build the input and target columns and thus obtain the same format as the [xP3](https://huggingface.co/datasets/bigscience/xP3) dataset by Muennighoff et al.
 
 
@@ -60,9 +70,9 @@ targets = str(xnli['train']['label'][i]).replace("0","vrai").replace("1","incert
 
 
 # Splits
-- train with X samples
-- dev with Y samples
-- test with Z samples
+- `train` with 8,639,444 samples
+- `valid` with 54,780 samples
+- `test` with 110,220 samples
 
 
 # How to use?