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
add Sensible banner (temp bull mascot)
Browse files
README.md
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# 🦉 Sensible — ModernBERT Sentiment Analysis
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### The modern replacement for the classic SST-2 sentiment model — **0.946 vs 0.913** on the exact same benchmark, one `pipeline()` line.
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<source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/ankit-aglawe/parable-assets/main/sensible_header_dark.png">
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<img alt="Sensible" src="https://raw.githubusercontent.com/ankit-aglawe/parable-assets/main/sensible_header.png">
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# 🦉 Sensible — ModernBERT Sentiment Analysis
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### The modern replacement for the classic SST-2 sentiment model — **0.946 vs 0.913** on the exact same benchmark, one `pipeline()` line.
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