Update README.md

README.md

@@ -4,19 +4,30 @@ language:
 license: 
 - other
 size_categories:
-- 10K<n<100K
+- 1M<n<10M
 task_categories:
 - text-classification
 tags:
 - stars-classification
+- DFP
+- french prompts
+annotations_creators:
+- found
+language_creators:
+- found
+multilinguality:
+- monolingual
+source_datasets:
+- amazon_reviews_multi
 ---
 
-# amazon_review_multi_fr_prompt_stars_classification
+
+# amazon_reviews_multi_fr_prompt_stars_classification
 ## Summary
 
-**amazon_review_multi_fr_prompt_stars_classification** is a subset of the [**Dataset of French Prompts (DFP)**]().
-It contains **X** rows that can be used for a stars-classification sentiment analysis task.
-The original data (without prompts) comes from the dataset [amazon_reviews_multi](https://huggingface.co/datasets/amazon_reviews_multi) by Keung et al. where only the French part has been kept.
+**amazon_reviews_multi_fr_prompt_stars_classification** is a subset of the [**Dataset of French Prompts (DFP)**](https://huggingface.co/datasets/CATIE-AQ/DFP).  
+It contains **4,620,000** rows that can be used for a stars-classification sentiment analysis task.  
+The original data (without prompts) comes from the dataset [amazon_reviews_multi](https://huggingface.co/datasets/amazon_reviews_multi) by Keung 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.
 
 
@@ -65,11 +76,10 @@ targets = arm['train']['stars'][i]
 ```
 
 
-
 # Splits
-- train with X samples
-- dev with Y samples
-- test with Z samples
+- `train` with 4,400,000 samples
+- `valid` with 110,000 samples
+- `test` with 110,000 samples
 
 
 # How to use?