Text Classification
Transformers
Safetensors
English
modernbert
sentiment-analysis
sentiment
sst-2
sst2
reviews
english
positive-negative
distilbert-sst2-alternative
text-embeddings-inference
Instructions to use AnkitAI/Sensible-ModernBERT-Sentiment-Analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AnkitAI/Sensible-ModernBERT-Sentiment-Analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AnkitAI/Sensible-ModernBERT-Sentiment-Analysis")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AnkitAI/Sensible-ModernBERT-Sentiment-Analysis") model = AutoModelForSequenceClassification.from_pretrained("AnkitAI/Sensible-ModernBERT-Sentiment-Analysis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Move the support section above the citation
Browse files
README.md
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Full fine-tune of ModernBERT-base on SST-2 (GLUE, 67k sentences): 2 epochs, lr 2e-5, batch 32, fp32, best checkpoint by held-back 5% of train — the official validation set stayed untouched until final reporting.
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## Citation
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```bibtex
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Full fine-tune of ModernBERT-base on SST-2 (GLUE, 67k sentences): 2 epochs, lr 2e-5, batch 32, fp32, best checkpoint by held-back 5% of train — the official validation set stayed untouched until final reporting.
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## Support the Project
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If this model is useful in your work, you can support independent research:
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<p align="left">
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<a href="https://www.buymeacoffee.com/AnkitAI" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me a Coffee" height="60" width="217" /></a>
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</p>
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## Citation
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```bibtex
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