Instructions to use facebook/data2vec-text-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/data2vec-text-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="facebook/data2vec-text-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("facebook/data2vec-text-base") model = AutoModel.from_pretrained("facebook/data2vec-text-base", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from facebook/data2vec-text-base: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/facebook/data2vec-text-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://facebook/data2vec-text-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/facebook/data2vec-text-base/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- d22d6e30e04f9903a36d4ba209029dd1230918296eed603a860e79ff029f82e5
- Size of remote file:
- 499 MB
- SHA256:
- 65f2e9bc8b76d5bfe3be263ce6d3a05a248c2d1e954fbc4b26dc96f592d53df3
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