Instructions to use DaniilOr/multilingual-factuality-estimation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DaniilOr/multilingual-factuality-estimation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DaniilOr/multilingual-factuality-estimation")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DaniilOr/multilingual-factuality-estimation") model = AutoModelForSequenceClassification.from_pretrained("DaniilOr/multilingual-factuality-estimation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from DaniilOr/multilingual-factuality-estimation: direct link, hf CLI and curl.
- Browser
- Download file 16.3 MB
-
https://huggingface.co/DaniilOr/multilingual-factuality-estimation/resolve/main/tokenizer.json
- Command line
-
hf download hf://DaniilOr/multilingual-factuality-estimation/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/DaniilOr/multilingual-factuality-estimation/resolve/main/tokenizer.json
16.3 MB
- Xet hash:
- c154d150c60ff11b7c537cec8414d7ed87853bad1786b450941aeac621f6ef7d
- Size of remote file:
- 16.3 MB
- SHA256:
- 5d731bb2acdda51bc71427b8c3104f37599df3999bd67c1bd84ff3591de48a3a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.