Instructions to use facebook/xlm-v-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/xlm-v-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="facebook/xlm-v-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("facebook/xlm-v-base") model = AutoModelForMaskedLM.from_pretrained("facebook/xlm-v-base", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from facebook/xlm-v-base: direct link, hf CLI and curl.
- Browser
- Download file 3.12 GB
-
https://huggingface.co/facebook/xlm-v-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://facebook/xlm-v-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/facebook/xlm-v-base/resolve/main/pytorch_model.bin
3.12 GB
- Xet hash:
- 135cc9f819a5a038713ef2d91dcab2fccb44a2966b817c5471b152c49d2eb007
- Size of remote file:
- 3.12 GB
- SHA256:
- 0d4e15d72f870336212b0b5c9e1ad11ed7814e99c6cbef06f569369331c3f78f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.