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")# 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
- Xet hash:
- 0791f5a0980057ffb34d5324907b0c1f254566b013e0ea92b3386cc526cc3f01
- Size of remote file:
- 499 MB
- SHA256:
- f5551f0cb91dc41241d621ff4743d1bebec55d0c24d5548e10dc40f7ab10322c
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