Instructions to use acul3/bert-large-mc4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use acul3/bert-large-mc4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="acul3/bert-large-mc4")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("acul3/bert-large-mc4") model = AutoModelForMaskedLM.from_pretrained("acul3/bert-large-mc4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from acul3/bert-large-mc4: direct link, hf CLI and curl.
- Browser
- Download file 1.34 GB
-
https://huggingface.co/acul3/bert-large-mc4/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://acul3/bert-large-mc4/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/acul3/bert-large-mc4/resolve/main/flax_model.msgpack
1.34 GB
- Xet hash:
- fd0307a04b27374492034d02dc7a7273b8fc61484356524aeac26f12529ed0c6
- Size of remote file:
- 1.34 GB
- SHA256:
- 70815ca9f2f04f51a05fd88fd2fb6ee746f9ea86cec7954735f955222e7fa348
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