Update README.md

README.md

@@ -4,18 +4,28 @@ language:
 license: 
 - mit
 size_categories:
-- 10K<n<100K
+- 100k<n<1M
 task_categories:
 - text-classification
 tags:
 - binary-sentiment-analysis
+- DFP
+- french prompts
+annotations_creators:
+- found
+language_creators:
+- found
+multilinguality:
+- monolingual
+source_datasets:
+- allocine
 ---
 
 # allocine_fr_prompt_sentiment_analysis
 ## Summary
 
-**allocine_fr_prompt_sentiment_analysis** is a subset of the [**Dataset of French Prompts (DFP)**]().  
-It contains **X** rows that can be used for a binary sentiment analysis task.  
+**allocine_fr_prompt_sentiment_analysis** is a subset of the [**Dataset of French Prompts (DFP)**](https://huggingface.co/datasets/CATIE-AQ/DFP).    
+It contains **5,600,000** rows that can be used for a binary sentiment analysis task.  
 The original data (without prompts) comes from the dataset [allocine](https://huggingface.co/datasets/allocine) by Blard.  
 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.
 
@@ -66,9 +76,9 @@ targets = str(allocine['train'][i]['label']).replace("0", "neg").replace("1","po
 
 
 # Splits
-- train with X samples
-- dev with Y samples
-- test with Z samples
+- `train` with 4,480,000 samples
+- `valid` with 560,000 samples
+- `test` with 560,000 samples
 
 
 # How to use?