Update README.md

README.md

@@ -3,6 +3,8 @@ license: mit
 base_model: xlm-roberta-base
 tags:
 - generated_from_trainer
+- NER
+- crypto
 metrics:
 - f1
 model-index:
@@ -24,18 +26,16 @@ It achieves the following results on the evaluation set:
 - F1: 0.9883
 
 ## Model description
+This model is a fine-tuned version of xlm-roberta-base, specializing in Named Entity Recognition (NER) within the cryptocurrency domain. It is optimized to recognize and classify entities such as cryptocurrency ticker symbols, names, and addresses within text.
 
-More information needed
-
-## Intended uses & limitations
-
-More information needed
+## Intended uses 
+Designed primarily for NER tasks in the cryptocurrency sector, this model excels in identifying and categorizing ticker symbols, cryptocurrency names, and addresses in textual content.
+## Limitations
+The model might not perform well in identifying and classifying entities that were not part of the training data or those that are less frequent in the cryptocurrency domain. It may also be sensitive to the context and format in which the entities are presented.
 
 ## Training and evaluation data
 
-More information needed
-
-## Training procedure
+The model was trained using a diverse dataset, including artificially generated tweets and ERC20 token metadata fetched through the [Covalent API](https://www.covalenthq.com/docs/unified-api/). GPT was employed to generate 500 synthetic tweets tailored for the cryptocurrency domain. The Covalent API was instrumental in obtaining a rich set of 20K+ unique ERC20 token metadata entries, enhancing the model's understanding and recognition of cryptocurrency entities.## Training procedure
 
 ### Training hyperparameters