Text Classification
Transformers
Safetensors
PyTorch
English
attentiontypes
custom-code
commonsense-reasoning
course-project
experimental
custom_code
Instructions to use owenarink/attentiontypes-commonsense with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use owenarink/attentiontypes-commonsense with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="owenarink/attentiontypes-commonsense", trust_remote_code=True)# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("owenarink/attentiontypes-commonsense", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload custom AttentionTypes classifier
Browse files
README.md
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This model is a custom `transformers` export of the `AttentionTypes` model from a course project on commonsense reasoning. The task is to choose the correct answer (`A`, `B`, or `C`) for a false sentence by comparing candidate repairs and selecting the most sensible one.
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The underlying model is a grouped BBPE transformer with DeBERTa-inspired disentangled attention. It was trained as a multiple-choice scoring model for a Kaggle-style commonsense challenge and exported here as a custom Hugging Face model for inference and reproducibility.
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## Model Description
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This model is a custom `transformers` export of the `AttentionTypes` model from a course project on commonsense reasoning. The task is to choose the correct answer (`A`, `B`, or `C`) for a false sentence by comparing candidate repairs and selecting the most sensible one.
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The underlying model is a grouped BBPE transformer with DeBERTa-inspired disentangled attention. It was trained as a multiple-choice scoring model for a Kaggle-style commonsense challenge and exported here as a custom Hugging Face model for inference and reproducibility.
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## Model Description
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