Text Classification
Transformers
PyTorch
English
deberta-v2
Trained with AutoTrain
text-embeddings-inference
Instructions to use whispAI/ClaimBuster-DeBERTaV2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use whispAI/ClaimBuster-DeBERTaV2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="whispAI/ClaimBuster-DeBERTaV2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("whispAI/ClaimBuster-DeBERTaV2") model = AutoModelForSequenceClassification.from_pretrained("whispAI/ClaimBuster-DeBERTaV2", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 183fc3a7fbbd9e3d95d9dc042b7e051b6db8190292b75c1648b04fd87c60764e
- Size of remote file:
- 738 MB
- SHA256:
- 2331f5fb19172e1f89bb7519d680d1de8d8ce1d6cce2f0b481f42476c62ac021
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