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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Metry63/attest-grounding-large") model = AutoModelForSequenceClassification.from_pretrained("Metry63/attest-grounding-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Metry63/attest-grounding-large: direct link, hf CLI and curl.
- Browser
- Download file 8.65 MB
-
https://huggingface.co/Metry63/attest-grounding-large/resolve/main/tokenizer.json
- Command line
-
hf download hf://Metry63/attest-grounding-large/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Metry63/attest-grounding-large/resolve/main/tokenizer.json
8.65 MB
File too large to display, you can check the raw version instead.