Token Classification
Transformers
Safetensors
English
modernbert
hallucination-detection
faithfulness
attribution
rag
Instructions to use ZaandaTeika/RAGHal-large-en-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ZaandaTeika/RAGHal-large-en-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ZaandaTeika/RAGHal-large-en-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ZaandaTeika/RAGHal-large-en-v1") model = AutoModelForTokenClassification.from_pretrained("ZaandaTeika/RAGHal-large-en-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from ZaandaTeika/RAGHal-large-en-v1: direct link, hf CLI and curl.
- Browser
- Download file 3.58 MB
-
https://huggingface.co/ZaandaTeika/RAGHal-large-en-v1/resolve/main/tokenizer.json
- Command line
-
hf download hf://ZaandaTeika/RAGHal-large-en-v1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/ZaandaTeika/RAGHal-large-en-v1/resolve/main/tokenizer.json
3.58 MB
File too large to display, you can check the raw version instead.