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")# 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
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Browse files
README.md
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- **v1** (2026-07-20) — initial release: ModernBERT-base, SST-2, seed 42.
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**Maker:** [Ankit Aglawe](https://ankitaglawe.com) — more on the Sensible series, benchmarks, and the build process at [ankitaglawe.com/sensible](https://ankitaglawe.com/sensible).
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- **v1** (2026-07-20) — initial release: ModernBERT-base, SST-2, seed 42.
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More on the Sensible models: [ankitaglawe.com/sensible](https://ankitaglawe.com/sensible)
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