Update README.md

README.md

@@ -1,4 +1,5 @@
 ---
+licence : cc-by-nc-4.0
 language:
 - fr
 size_categories:
@@ -7,14 +8,24 @@ task_categories:
 - text-classification
 tags:
 - textual-entailment
+- DFP
+- french prompts
+annotations_creators:
+- found
+language_creators:
+- found
+multilinguality:
+- monolingual
+source_datasets:
+- allocine
 ---
 
 # anli_fr_prompt_textual_entailment
 ## Summary
 
-**anli_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 anli French part has been kept.
+**anli_fr_prompt_textual_entailment** is a subset of the [**Dataset of French Prompts (DFP)**](https://huggingface.co/datasets/CATIE-AQ/DFP).  
+It contains **550,000** 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 anli 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 +70,9 @@ targets = str(anli['label'][i]).replace("0","vrai").replace("1","incertain").rep
 
 
 # Splits
-- train with X samples
-- dev with Y samples
-- test with Z samples
+- `train` with 550,000 samples
+- no `valid` split
+- no `test` split
 
 
 # How to use?
@@ -87,3 +98,6 @@ dataset = load_dataset("CATIE-AQ/anli_fr_prompt_textual_entailment")
 
 ## This Dataset
 
+
+## License 
+cc-by-nc-4.0