Update README.md

README.md

@@ -28,7 +28,8 @@ probably proofread and complete it, then remove this comment. -->
 
 # i-be-snek/distilbert-base-uncased-finetuned-ner-exp_B
 
-This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the English subset of the NER [Babelscape/multinerd](https://huggingface.co/datasets/Babelscape/multinerd) dataset. Only five entities were considered: Person (PER), Animal (ANIM), Organization (ORG), Location (LOC), and Disease (DIS). The dataset was modified so that all other named entities were swapped with the '0' label ID. 
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the English subset of the NER [Babelscape/multinerd](https://huggingface.co/datasets/Babelscape/multinerd) dataset.
+
 It achieves the following results on the evaluation set:
 - Train Loss: 0.0084
 - Validation Loss: 0.0587
@@ -42,11 +43,10 @@ All scripts for training can be found in this [GitHub repository](https://github
 
 ## Model description
 
-More information needed
-
-## Intended uses & limitations
-
-More information needed
+[distilbert-base-uncased-finetuned-ner-exp_B](https://huggingface.co/i-be-snek/distilbert-base-uncased-finetuned-ner-exp_B) is a Named Entity Recognition model finetuned on [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased). 
+This model is uncased, so it makes no distinction between "sarah" and "Sarah". 
+The dataset it was fine-tuned on was modified. Only five entities were considered: Person (PER), Animal (ANIM), Organization (ORG), Location (LOC), and Disease (DIS).
+The dataset was modified further so that all other named entities not included in this list were swapped with the '0' label ID. Tokens IDs were also re-indexed. 
 
 ## Training and evaluation data