Instructions to use KnutJaegersberg/claim_extraction_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KnutJaegersberg/claim_extraction_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KnutJaegersberg/claim_extraction_classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KnutJaegersberg/claim_extraction_classifier") model = AutoModelForSequenceClassification.from_pretrained("KnutJaegersberg/claim_extraction_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from KnutJaegersberg/claim_extraction_classifier: direct link, hf CLI and curl.
- Browser
- Download file 587 Bytes
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https://huggingface.co/KnutJaegersberg/claim_extraction_classifier/resolve/main/README.md
- Command line
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hf download hf://KnutJaegersberg/claim_extraction_classifier/README.md
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curl -L -o README.md https://huggingface.co/KnutJaegersberg/claim_extraction_classifier/resolve/main/README.md
587 Bytes
metadata
license: mit
pipeline_tag: text-classification
tags:
- deberta
datasets:
- KnutJaegersberg/FEVER_claim_extraction
deberta-v3-large trained for one epoch (properly underfitted) on a dataset that combined FEVER data with externally sourced non-claims. Label 0: no claim Label 1: claim
Paper of the data: "Claim extraction from text using transfer learning" - By Acharya Ashish Prabhakar, Salar Mohtaj, Sebastian Möller
https://aclanthology.org/2020.icon-main.39/
Performance on held out data:
Accuracy 0.8128525 F1_Score 0.854962 mcc 0.6173648
properly is of use already