metadata
language: en
license: apache-2.0
tags:
- text-classification
- sentiment-analysis
- roberta
- pytorch
- transformers
datasets:
- imdb
- glue
- tweet_eval
- yelp_review_full
pipeline_tag: text-classification
🎭 Sentiment Analysis — airzipm
A powerful 3-class sentiment analysis model fine-tuned from roberta-base
on a combined corpus of 200 000+ samples spanning movie reviews, short sentences,
tweets, and restaurant reviews.
🏷️ Labels
| ID | Label | Description |
|---|---|---|
| 0 | Negative | Negative sentiment / opinion |
| 1 | Neutral | Neutral / mixed sentiment |
| 2 | Positive | Positive sentiment / opinion |
📊 Performance
| Metric | Value |
|---|---|
| Val Accuracy | 0.8239 |
| Val F1 (macro) | 0.7827 |
🚀 Quick Usage
from transformers import pipeline
classifier = pipeline(
"text-classification",
model="airzipm/sentiment-analysis-roberta",
)
# Single prediction
print(classifier("This movie was absolutely amazing!"))
# [{'label': 'Positive', 'score': 0.97}]
# Batch prediction
texts = [
"Great product, highly recommend!",
"It was okay, nothing special.",
"Terrible experience, waste of money.",
]
for t, r in zip(texts, classifier(texts)):
print(f"{t[:45]:50s} → {r['label']} ({r['score']:.1%})")
🛠️ Training Details
| Setting | Value |
|---|---|
| Base model | roberta-base |
| Max token length | 128 |
| Batch size | 32 |
| Learning rate | 2e-5 |
| Optimizer | AdamW + warmup |
| Mixed precision | FP16 |
| Label smoothing | 0.1 |
| Class weights | Balanced |
📦 Training Data
| Dataset | Domain | Samples |
|---|---|---|
| IMDB | Movie reviews | 50 000 |
| SST-2 | Short sentences | 50 000 |
| Tweet Eval | Twitter posts | 50 000 |
| Yelp Review | Business review | 50 000 |
🖼️ Training Curves & Confusion Matrix
See training_curves.png and confusion_matrix.png in this repository.
👤 Author
Created by airzipm — Hugging Face Profile