Instructions to use jasonwu/ToD-BERT-jnt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jasonwu/ToD-BERT-jnt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="jasonwu/ToD-BERT-jnt")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("jasonwu/ToD-BERT-jnt") model = AutoModelForMaskedLM.from_pretrained("jasonwu/ToD-BERT-jnt", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from jasonwu/ToD-BERT-jnt: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/jasonwu/ToD-BERT-jnt/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://jasonwu/ToD-BERT-jnt/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/jasonwu/ToD-BERT-jnt/resolve/main/flax_model.msgpack
438 MB
- Xet hash:
- 0df6e822c6ef252b4c86fedd7853b0c9504f8b8ead4a0aee10db89318c0754a8
- Size of remote file:
- 438 MB
- SHA256:
- bec5848a1c02b8181d2b16c987a827aa8d3da4bc8464e0bf2f0702b0c24ee984
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