distilbert-imdb-finetuned

DistilBERT Fine-Tuned on IMDB Sentiment Dataset

This model is a fine-tuned version of distilbert-base-uncased on the IMDB movie reviews dataset for binary sentiment classification (positive/negative).

Model Details

• Base model: distilbert-base-uncased
• Dataset: IMDB (25k train + 25k test reviews)
• Labels:
  • 0 → negative
  • 1 → positive

How to Use

from transformers import pipeline

clf = pipeline("text-classification", model="BhuviMohan/distilbert-imdb-finetuned")
print(clf("The movie was absolutely fantastic!"))

## Training Procedure

- Epochs: 1

- Batch size: 8

- Learning rate: 2e-5

- Optimizer: AdamW

- Hardware: CPU training (Windows)

- Results (Evaluation)

**Accuracy: ~1.0 (on 2k subset)**


Save the file.

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# ✅ **STEP 5 — Upload model to Hugging Face Hub**

Make sure your token is created with **Write access**.

Then login (if not logged in):

```bash
huggingface-cli login