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")# pip install -U transformers accelerate # 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
Download threshold.json from GLLhJpFfYB/claim-gate-mdeberta: direct link, hf CLI and curl.
- Browser
- Download file 172 Bytes
-
https://huggingface.co/GLLhJpFfYB/claim-gate-mdeberta/resolve/main/threshold.json
- Command line
-
hf download hf://GLLhJpFfYB/claim-gate-mdeberta/threshold.json
-
curl -L -o threshold.json https://huggingface.co/GLLhJpFfYB/claim-gate-mdeberta/resolve/main/threshold.json
172 Bytes
| { | |
| "threshold": 0.5, | |
| "model_key": "mdeberta", | |
| "model_name": "microsoft/mdeberta-v3-base", | |
| "max_length": 64, | |
| "labels": { | |
| "0": "no-claim", | |
| "1": "claim" | |
| } | |
| } |