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 tokenizer.json from facebook/xlm-v-base: direct link, hf CLI and curl.
- Browser
- Download file 61.4 MB
-
https://huggingface.co/facebook/xlm-v-base/resolve/main/tokenizer.json
- Command line
-
hf download hf://facebook/xlm-v-base/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/facebook/xlm-v-base/resolve/main/tokenizer.json
61.4 MB
- Xet hash:
- 1d01e4258710a17cb01346e6f779de3f171fc9acea2bad1a8d3228c8a65857d0
- Size of remote file:
- 61.4 MB
- SHA256:
- 43f949ef0c0b1ac18c506551109383d4a8a2bd48956ced0cc95cfd9120d42dca
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.