Text Classification
Transformers
ONNX
Safetensors
deberta-v2
claim-detection
claim-verification
deberta
Eval Results (legacy)
text-embeddings-inference
Instructions to use GLLhJpFfYB/claim-gate-mdeberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GLLhJpFfYB/claim-gate-mdeberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="GLLhJpFfYB/claim-gate-mdeberta")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("GLLhJpFfYB/claim-gate-mdeberta") model = AutoModelForSequenceClassification.from_pretrained("GLLhJpFfYB/claim-gate-mdeberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2c5524b801d51e16bf0a65b4a2c576e87fed145253aa171f22b70b681123dbf6
- Size of remote file:
- 5.84 kB
- SHA256:
- c9ae0fbb7abecdc19f5e2ba38bb358ef012907f36b571f299375c62ca45983c8
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