Text Classification
Transformers
Safetensors
PyTorch
English
roberta
sentiment-analysis
fastapi
multilingual
docker
kafka
nlp
Eval Results (legacy)
Instructions to use SkyNet-DL/sentiment-roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SkyNet-DL/sentiment-roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SkyNet-DL/sentiment-roberta")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SkyNet-DL/sentiment-roberta") model = AutoModelForSequenceClassification.from_pretrained("SkyNet-DL/sentiment-roberta") - Notebooks
- Google Colab
- Kaggle
Add Youtube demo link
Browse files
README.md
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- config files
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- vocabulary files
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making the model fully compatible with Hugging Face Transformers.
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- config files
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- vocabulary files
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making the model fully compatible with Hugging Face Transformers.
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## 🎥 YouTube API Demo
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A full video demonstration of the Sentiment Analysis API is also available on YouTube.
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The video showcases:
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- How to launch the FastAPI inference service
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- How to use the web interface
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- Real-time sentiment prediction testing
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- Multilingual text inference
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- API response behavior
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- End-to-end model interaction
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📺 Watch the demo here:
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https://www.youtube.com/watch?v=ExuS0F8i0Wo
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This demo provides a quick overview of how the deployed inference system works in practice.
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