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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Download README.md from sofiane-isi/distilbert-sentiment: direct link, hf CLI and curl.
- Browser
- Download file 792 Bytes
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https://huggingface.co/sofiane-isi/distilbert-sentiment/resolve/main/README.md
- Command line
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hf download hf://sofiane-isi/distilbert-sentiment/README.md
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curl -L -o README.md https://huggingface.co/sofiane-isi/distilbert-sentiment/resolve/main/README.md
792 Bytes
metadata
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.