Text Classification
Transformers
Safetensors
English
deberta-v2
hallucination-detection
groundedness
rag
nli
fact-checking
text-embeddings-inference
Instructions to use Metry63/attest-grounding-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Metry63/attest-grounding-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Metry63/attest-grounding-large")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Metry63/attest-grounding-large") model = AutoModelForSequenceClassification.from_pretrained("Metry63/attest-grounding-large") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6bd079d8585fbcdbf47cb3a534f888b5aaa32b8994301f5c47573e6cf825d7da
- Size of remote file:
- 2.46 MB
- SHA256:
- c679fbf93643d19aab7ee10c0b99e460bdbc02fedf34b92b05af343b4af586fd
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