Text Classification
Transformers
PyTorch
English
deberta-v2
claim-detection
text-embeddings-inference
Instructions to use Nithiwat/mdeberta-v3-base_claimbuster with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nithiwat/mdeberta-v3-base_claimbuster with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Nithiwat/mdeberta-v3-base_claimbuster")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Nithiwat/mdeberta-v3-base_claimbuster") model = AutoModelForSequenceClassification.from_pretrained("Nithiwat/mdeberta-v3-base_claimbuster") - Notebooks
- Google Colab
- Kaggle
This is the mDeBERTa model finetuned on the ClaimBuster dataset. It is used for claim detection and has an accuracy of 83%.
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