Instructions to use mathislucka/deberta-base-hallucination-eval-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mathislucka/deberta-base-hallucination-eval-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mathislucka/deberta-base-hallucination-eval-v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mathislucka/deberta-base-hallucination-eval-v2") model = AutoModelForSequenceClassification.from_pretrained("mathislucka/deberta-base-hallucination-eval-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from mathislucka/deberta-base-hallucination-eval-v2: direct link, hf CLI and curl.
- Browser
- Download file 1.48 GB
-
https://huggingface.co/mathislucka/deberta-base-hallucination-eval-v2/resolve/refs%2Fpr%2F1/optimizer.pt
- Command line
-
hf download hf://mathislucka/deberta-base-hallucination-eval-v2@refs/pr/1/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/mathislucka/deberta-base-hallucination-eval-v2/resolve/refs%2Fpr%2F1/optimizer.pt
1.48 GB
- Xet hash:
- b2f37fcfa5a3feaffe7220408eccb2b76359df3f72ec37f05a4bdef987225c03
- Size of remote file:
- 1.48 GB
- SHA256:
- 694b621d30efc5917a05b1591f14c19c5e212323c7bcfebfac30755e7d4429b5
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