Update README.md

README.md

@@ -1,20 +1,31 @@
 ---
+licence: mit
 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
 ---
 
 # mnli_fr_prompt_textual_entailment
 ## Summary
 
-**mnli_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 mnli French part has been kept.
+**mnli_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 mnli 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.
 
 
@@ -51,7 +62,7 @@ A list of prompts (see below) was then applied in order to build the input and t
 In the prompt list above, `premise`, `hypothesis` and `targets` have been constructed from:
 ```
 moritz = load_dataset('MoritzLaurer/multilingual-NLI-26lang-2mil7')
-mnli = moritz['fr_anli']
+mnli = moritz['fr_mnli']
 mnli['premise'] = list(map(lambda i: i.replace(' . ','. ').replace(' .','. ').replace('( ','(').replace(' )',')').replace(' , ',', ').replace(', ',', ').replace("' ","'"), map(str,mnli['premise'])))
 mnli['hypothesis'] = list(map(lambda x: x.replace(' . ','. ').replace(' .','. ').replace('( ','(').replace(' )',')').replace(' , ',', ').replace(', ',', ').replace("' ","'"), map(str,mnli['hypothesis'])))
 targets = str(anli['label'][i]).replace("0","vrai").replace("1","incertain").replace("2","faux")
@@ -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 +98,7 @@ dataset = load_dataset("CATIE-AQ/mnli_fr_prompt_textual_entailment")
 
 ## This Dataset
 
+
+
+## License 
+mit