Update README.md

README.md

@@ -1,5 +1,6 @@
 ---
 language: en
+license: apache-2.0
 ---
 
 # Women's Clothing Reviews Sentiment Analysis with DistilBERT
@@ -11,25 +12,27 @@ This Hugging Face repository contains a fine-tuned DistilBERT model for sentimen
 ## Model Details
 
 - **Model Architecture**: Fine-tuned DistilBERT
-- **Sentiment Categories**: Neutral[0], Negative[1], Positive[2]
+- **Sentiment Categories**: Neutral [0], Negative [1], Positive [2]
 - **Input Format**: Text-based clothing reviews
 - **Output Format**: Sentiment category labels
 
-## Usage
 
+## Training result
+It achieves the following results on the evaluation set:
+- **Validation Loss**: 1.1677
+
+ 
 ## Installation
 
 To use this model, you'll need to install the Hugging Face Transformers library and any additional dependencies.
+- **pip install transformers**
+- **pip install torch**
 
-```bash
-pip install transformers
-pip install torch
 
-Usage
+## Usage
 You can easily load the pre-trained model for sentiment analysis using Hugging Face's DistilBertForSequenceClassification and DistilBertTokenizerFast.
 
-python
-Copy code
+```bash
 from transformers import DistilBertForSequenceClassification, DistilBertTokenizerFast
 import torch
 
@@ -37,3 +40,8 @@ model_name = "ongaunjie/distilbert-cloths-sentiment"
 tokenizer = DistilBertTokenizerFast.from_pretrained(model_name)
 model = DistilBertForSequenceClassification.from_pretrained(model_name)
 
+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))