Update README.md

README.md

@@ -2,7 +2,11 @@
 license: apache-2.0
 base_model: bert-base-uncased
 tags:
-- generated_from_trainer
+- 'biology '
+- NLP
+- text-classification
+- drugs
+- BERT
 metrics:
 - accuracy
 - precision
@@ -11,11 +15,11 @@ metrics:
 model-index:
 - name: bert-drug-review-to-condition
   results: []
+language:
+- en
+library_name: transformers
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
 # bert-drug-review-to-condition
 
 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
@@ -28,17 +32,19 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+Fine-tuning of Bert model with drug-related data for the purpose of text classification
 
 ## Intended uses & limitations
 
-More information needed
+Personal project.
 
 ## Training and evaluation data
 
-More information needed
+Kallumadi,Surya and Grer,Felix. (2018). Drug Reviews (Drugs.com). UCI Machine Learning Repository. https://doi.org/10.24432/C5SK5S.
 
 ## Training procedure
+Multiclass classification
+The model predicts the 'condition' feature from the 'review' feature, only the first 21 conditions are selected.
 
 ### Training hyperparameters
 
@@ -65,4 +71,4 @@ The following hyperparameters were used during training:
 - Transformers 4.40.0
 - Pytorch 2.2.1+cu121
 - Datasets 2.19.0
-- Tokenizers 0.19.1
+- Tokenizers 0.19.1