Instructions to use yfqiu-nlp/mFACT-fr_XX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yfqiu-nlp/mFACT-fr_XX with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="yfqiu-nlp/mFACT-fr_XX")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("yfqiu-nlp/mFACT-fr_XX") model = AutoModelForSequenceClassification.from_pretrained("yfqiu-nlp/mFACT-fr_XX", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from yfqiu-nlp/mFACT-fr_XX: direct link, hf CLI and curl.
- Browser
- Download file 711 MB
-
https://huggingface.co/yfqiu-nlp/mFACT-fr_XX/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://yfqiu-nlp/mFACT-fr_XX/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/yfqiu-nlp/mFACT-fr_XX/resolve/main/pytorch_model.bin
711 MB
- Xet hash:
- ba853bded7b78d96168761ea2ed8365c259bcb2adf1e71770d657dfeca09ad83
- Size of remote file:
- 711 MB
- SHA256:
- e2bf1160ac5202ec8e59361004a1cc43076f3e5de791beae99dc3a2f98a86252
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.