Update README.md

README.md

@@ -16,11 +16,20 @@ This Hugging Face repository contains a fine-tuned DistilBERT model for sentimen
 - **Input Format**: Text-based clothing reviews
 - **Output Format**: Sentiment category labels
 
+## Fine-tuning procedure
+This model was fine-tuned using a relatively small dataset containing 23487 rows broken down into train/eval/test dataset. Nevertheless, the fine-tuned model was able to performs slightly better than the base-distilbert-model on the test dataset.
+
 
 ## Training result
-It achieves the following results on the evaluation set:
+It achieved the following results on the evaluation set:
 - **Validation Loss**: 1.1677
 
+### Comparison between the base distilbert model VS fine-tuned distilbert
+| Model          | Accuracy | Precision | Recall | F1 Score |
+|--------------- | -------- | --------- | ------ | -------- |
+| DistilBERT base model    | 0.79     | 0.77      | 0.79   | 0.77     |
+| DistilBERT fine-tuned     | 0.85     | 0.86      | 0.85   | 0.85     |
+
  
 ## Installation
 
@@ -32,7 +41,7 @@ To use this model, you'll need to install the Hugging Face Transformers library
 ## Usage
 You can easily load the pre-trained model for sentiment analysis using Hugging Face's DistilBertForSequenceClassification and DistilBertTokenizerFast.
 
-```bash
+```python
 from transformers import DistilBertForSequenceClassification, DistilBertTokenizerFast
 import torch
 
@@ -44,4 +53,4 @@ review = "This dress is amazing, I love it!"
 inputs = tokenizer.encode(review, return_tensors="pt")
 with torch.no_grad():
     outputs = model(inputs)
-predicted_class = int(torch.argmax(outputs.logits))
+predicted_class = int(torch.argmax(outputs.logits))