Text Classification
Transformers
Safetensors
distilbert
sentiment-analysis
text-embeddings-inference
Instructions to use sofiane-isi/distilbert-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sofiane-isi/distilbert-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sofiane-isi/distilbert-sentiment")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sofiane-isi/distilbert-sentiment") model = AutoModelForSequenceClassification.from_pretrained("sofiane-isi/distilbert-sentiment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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library_name: transformers
pipeline_tag: text-classification
tags:
- text-classification
- sentiment-analysis
- distilbert
datasets:
- cornell-movie-review-data/rotten_tomatoes
---
# DistilBERT Sentiment Classifier
DistilBERT fine-tuned for sentiment analysis on Rotten Tomatoes movie reviews.
## Repository
`sofiane-isi/distilbert-sentiment`
## Dataset
`cornell-movie-review-data/rotten_tomatoes`
Classes:
- NEGATIVE
- POSITIVE
## Evaluation
- Accuracy: 0.8461538461538461
- Evaluation loss: 0.42963317036628723
## Base model
`distilbert-base-uncased`
## MLOps workflow
Dataset → tokenization → fine-tuning → evaluation → Hugging Face Hub → Model Card → Space.
## Limitations
Educational binary sentiment classifier fine-tuned specifically on Rotten Tomatoes.
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