Update README.md

README.md

@@ -8,14 +8,24 @@ task_categories:
 - token-classification
 tags:
 - pos
+- DFP
+- french prompts
+annotations_creators:
+- found
+language_creators:
+- found
+multilinguality:
+- monolingual
+source_datasets:
+- universal_dependencies_fr_gsd
 ---
 
 # universal_dependencies_fr_gsd_fr_prompt_pos
 ## Summary
 
-**universal_dependencies_fr_gsd_fr_prompt_pos** is a subset of the [**Dataset of French Prompts (DFP)**]().
-It contains **X** rows that can be used for a part-of-speech task.
-The original data (without prompts) comes from the dataset [universal_dependencies](https://huggingface.co/datasets/universal_dependencies) where only the French gsd split has been kept.
+**universal_dependencies_fr_gsd_fr_prompt_pos** is a subset of the [**Dataset of French Prompts (DFP)**](https://huggingface.co/datasets/CATIE-AQ/DFP).  
+It contains **343,161** rows that can be used for a part-of-speech task.  
+The original data (without prompts) comes from the dataset [universal_dependencies](https://huggingface.co/datasets/universal_dependencies) where only the French gsd split 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.
 
 
@@ -60,9 +70,9 @@ fr_gsd['train']['upos'] = list(map(lambda x: x.replace("[","").replace("]","").r
 
 
 # Splits
-- train with X samples
-- dev with Y samples
-- test with Z samples
+- `train` with 303,429 samples
+- `valid` with 30,996 samples
+- `test` with 8,736 samples
 
 
 # How to use?