Update README.md

README.md

@@ -13,26 +13,20 @@ pipeline_tag: text-classification
 
 This is a text classification model based on DistilBERT. It has been fine-tuned on the ecommerce_reviews_with_language_drift dataset.
 
-## Model Description
-
-Provide a detailed description of your model here. Explain what the model does, how it was trained, and any specific considerations.
-
 ## Intended Use
 
-Describe the intended use cases and limitations of your model. Include information about the types of inputs it is designed to handle and the expected outputs.
+The model is used for classifying product reviews in text format. The probable outputs are 'positive', 'negative' and 'neutral'.
 
 ## Training Data
 
-Include details about the dataset used for training. Mention any preprocessing steps and why this dataset was chosen.
+The arize-ai/ecommerce_reviews_with_language_drift dataset was used for training. Only the 'text' and 'label' columns were used. The training dataset contains 8k rows out of which 34.1% are labeled 'positive', 33.4 % are labeled 'negative' and 32.5% are labeled 'neutral'. So it is a balanced dataset.
 
 ## Evaluation
 
-Discuss the evaluation metrics and the performance of the model. Provide any relevant results or benchmarks.
+The model was fine tuned based on the F1 score for 50 epochs. The best score obtained was 0.67.
 
 ## Example Usage
 
-Provide a code snippet showing how to use the model. This helps users quickly understand how to implement it.
-
 ```python
 from transformers import pipeline