Text Classification
Transformers
Safetensors
English
bert
question-answering
evaluation
text
text-embeddings-inference
Instructions to use zli12321/answer_equivalence_tiny_bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zli12321/answer_equivalence_tiny_bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="zli12321/answer_equivalence_tiny_bert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("zli12321/answer_equivalence_tiny_bert") model = AutoModelForSequenceClassification.from_pretrained("zli12321/answer_equivalence_tiny_bert") - Notebooks
- Google Colab
- Kaggle
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README.md
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[](https://pypi.org/project/qa-metrics/)
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[](https://colab.research.google.com/drive/1Ke23KIeHFdPWad0BModmcWKZ6jSbF5nI?usp=sharing)
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> A fast and lightweight Python package for evaluating question-answering models and prompting of black-box and open-source large language models.
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## 🎉 Latest Updates
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[](https://pypi.org/project/qa-metrics/)
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[](https://colab.research.google.com/drive/1Ke23KIeHFdPWad0BModmcWKZ6jSbF5nI?usp=sharing)
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> Check out the main [Repo](https://github.com/zli12321/qa_metrics)
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> A fast and lightweight Python package for evaluating question-answering models and prompting of black-box and open-source large language models.
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## 🎉 Latest Updates
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