Text Classification
Transformers
TensorBoard
Safetensors
English
distilbert
sentiment-analysis
imdb
fine-tuned
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("BhuviMohan/distilbert-imdb-finetuned")
model = AutoModelForSequenceClassification.from_pretrained("BhuviMohan/distilbert-imdb-finetuned")Quick Links
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
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BhuviMohan/distilbert-imdb-finetuned")