Instructions to use RAYZ/macbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RAYZ/macbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="RAYZ/macbert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("RAYZ/macbert") model = AutoModel.from_pretrained("RAYZ/macbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from RAYZ/macbert: direct link, hf CLI and curl.
- Browser
- Download file 409 MB
-
https://huggingface.co/RAYZ/macbert/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://RAYZ/macbert@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/RAYZ/macbert/resolve/refs%2Fpr%2F1/pytorch_model.bin
409 MB
- Xet hash:
- da91851e06e03aba64060bedabf2e38efb1ac7922fb798bc059340f255e27717
- Size of remote file:
- 409 MB
- SHA256:
- 0cc2f971e78222f926f27167c4eb76703625029333827805f6543d6c99ca9c29
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