# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("KnutJaegersberg/claim_extraction_classifier")
model = AutoModelForSequenceClassification.from_pretrained("KnutJaegersberg/claim_extraction_classifier")Quick Links
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
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KnutJaegersberg/claim_extraction_classifier")