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distilb-sentiment-best
HarshGahlaut/distilb-sentiment-best
distilb-sentiment-best is a text classification model from HarshGahlaut. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
This model is a fine-tuned version of distilbert-base-uncased for binary sentiment classification (positive/negative). It achieves approximately 90% F1 score on the IMDb dataset.
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Public
Updated Nov 15, 2025
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Access
- Public
- Created
- Nov 15, 2025
- Updated
- Nov 15, 2025
- SHA
- 7cc40ff2
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Base models
Model card
From the Hugging Face model README
DistilBERT Sentiment Classifier (Hybrid-Sentiment-Engine)
This model is a fine-tuned version of distilbert-base-uncased for binary sentiment classification (positive/negative).
It achieves approximately 90% F1 score on the IMDb dataset.
The model is part of the Hybrid-Sentiment-Engine, a project combining classical ML (TF-IDF + Logistic Regression) with transformer-based deep learning for flexible, high-accuracy sentiment analysis.
🔥 Model Details
- Base model: distilbert-base-uncased
- Task: Sentiment Analysis (binary)
- Dataset: IMDb reviews
- Languages: English
- Framework: Hugging Face Transformers
- License: MIT
📊 Performance
| Metric | Score |
|---|---|
| F1 Score | ~90% |
| Accuracy | ~90% |
| Precision | ~89% |
| Recall | ~90% |
📥 Usage
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_id = "HarshGahlaut/distilb-sentiment-best"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Languages
- en
- Created
- Nov 15, 2025
- Updated
- Nov 15, 2025