Text Classification
Transformers
English
prompting
zero-shot
few-shot
football
sentiment
adaptive-retrieval
Instructions to use kevinkyi/Homework2_Multishot_Prompting with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kevinkyi/Homework2_Multishot_Prompting with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kevinkyi/Homework2_Multishot_Prompting")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("kevinkyi/Homework2_Multishot_Prompting", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Add Method Card
Browse files
README.md
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## TL;DR
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We compare zero-shot, adaptive one-shot, and adaptive 5-shot prompting for binary sentiment on football news.
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---
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library_name: transformers
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pipeline_tag: text-classification
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license: mit
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tags:
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- prompting
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- zero-shot
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- few-shot
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- football
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- sentiment
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- adaptive-retrieval
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model_name: Football Sentiment Prompting (0/1/5-shot)
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language:
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- en
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datasets:
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- james-kramer/football_news
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inference: false
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---# Method Card — Football Sentiment Prompting (0/1/5-shot)
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## TL;DR
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We compare zero-shot, adaptive one-shot, and adaptive 5-shot prompting for binary sentiment on football news.
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