Update README.md

README.md

@@ -2,19 +2,29 @@
 language:
 - fr
 size_categories:
-- 10K<n<100K
+- 100K<n<1M
 task_categories:
 - text-classification
 tags:
 - textual-entailment
+- DFP
+- french prompts
+annotations_creators:
+- found
+language_creators:
+- found
+multilinguality:
+- monolingual
+source_datasets:
+- multilingual-NLI-26lang-2mil7
 ---
 
 # ling_fr_prompt_textual_entailment
 ## Summary
 
-**ling_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 [multilingual-NLI-26lang-2mil7](https://huggingface.co/datasets/MoritzLaurer/multilingual-NLI-26lang-2mil7) by Laurer et al. where only the ling French part has been kept.
+**ling_fr_prompt_textual_entailment** is a subset of the [**Dataset of French Prompts (DFP)**](https://huggingface.co/datasets/CATIE-AQ/DFP).  
+It contains **110,000** rows that can be used for a textual entailment task.  
+The original data (without prompts) comes from the dataset [multilingual-NLI-26lang-2mil7](https://huggingface.co/datasets/MoritzLaurer/multilingual-NLI-26lang-2mil7) by Laurer et al. where only the ling 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.
 
 
@@ -59,9 +69,9 @@ targets = str(ling['label'][i]).replace("0","vrai").replace("1","incertain").rep
 
 
 # Splits
-- train with X samples
-- dev with Y samples
-- test with Z samples
+- `train` with 110,000 samples
+- no `valid` split
+- no `test` split
 
 
 # How to use?
@@ -87,3 +97,6 @@ dataset = load_dataset("CATIE-AQ/ling_fr_prompt_textual_entailment")
 
 ## This Dataset
 
+
+## License 
+cc-by-nc-4.0