READ

label_map = {"LABEL_0": "negative", "LABEL_1": "positive"}
out = clf("I hated this movie.")[0]
print(label_map[out["label"]], out["score"])

README.md

@@ -1,37 +1,62 @@
-# Fine-tuned BERT for IMDB Sentiment Classification
+---
+language:
+- en
+tags:
+- sentiment-analysis
+- text-classification
+- bert
+- manav
+- ManavDhayeCoder/sentiment-bert
+- ManavDhaye
+pipeline_tag: text-classification
+base_model:
+- google-bert/bert-base-uncased
+datasets:
+- imdb
+library_name: transformers
+widget:
+- text: This movie was amazing!
+- text: Worst movie I have ever seen.
+model-index:
+- name: sentiment-bert
+  results: []
+metrics:
+- accuracy
+---
 
-**Model:** google-bert/bert-base-uncased → fine-tuned on IMDB (binary sentiment)
+# 📘 BERT Sentiment Analysis Model (Fine-Tuned on IMDB)
 
-**Task:** Sequence classification (sentiment analysis)
+This model is a fine-tuned version of **google-bert/bert-base-uncased**, trained on the **IMDB movie reviews dataset** for binary sentiment classification.
 
-**Author:** Your Name / @YourHFusername  
-**License:** Apache-2.0 (choose appropriate license)
+It predicts whether text expresses **negative** or **positive** sentiment.
+
+This model is hosted by **[@ManavDhayeCoder](https://huggingface.co/ManavDhayeCoder)**.
 
 ---
 
-## Description
-This is a BERT-base model fine-tuned on the IMDB movie reviews dataset for binary sentiment classification (positive / negative).  
-It accepts raw text and returns a label and confidence score.
+# 🚀 Model Overview
+
+| Property | Value |
+|----------|--------|
+| **Base model** | google-bert/bert-base-uncased |
+| **Task** | Sentiment Analysis (Sequence Classification) |
+| **Labels** | negative / positive |
+| **Dataset** | IMDB |
+| **Library** | Hugging Face Transformers |
+| **Format** | model.safetensors |
 
-**Input:** string (movie review)  
-**Output:** dictionary with `label` and `score`. By default the model may return `LABEL_0` and `LABEL_1`.  
-Use the `id2label` mapping below to convert to human-readable labels:  
-- `LABEL_0` → `negative`  
-- `LABEL_1` → `positive`
+The model has two classes:
+
+- `LABEL_0` → **negative**  
+- `LABEL_1` → **positive**
 
 ---
 
-## Example usage
+# 🔥 Quick Usage Example
+
 ```python
 from transformers import pipeline
 
-clf = pipeline("text-classification", model="YourHFusername/sentiment-bert")
-
-print(clf("I loved this movie!"))
-# [{'label': 'LABEL_1', 'score': 0.998}]
+clf = pipeline("text-classification", model="ManavDhayeCoder/sentiment-bert")
 
-# map to human-readable label
-map = { "LABEL_0": "negative", "LABEL_1": "positive" }
-out = clf("I loved this movie!")[0]
-print(map[out["label"]], out["score"])
-# positive 0.998
+print(clf("This movie was amazing!"))