Instructions to use browndw/docusco-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use browndw/docusco-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="browndw/docusco-bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("browndw/docusco-bert") model = AutoModelForTokenClassification.from_pretrained("browndw/docusco-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from browndw/docusco-bert: direct link, hf CLI and curl.
- Browser
- Download file 431 MB
-
https://huggingface.co/browndw/docusco-bert/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://browndw/docusco-bert/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/browndw/docusco-bert/resolve/main/flax_model.msgpack
431 MB
- Xet hash:
- 5d90d9d58568a11aa0576272c133f4e4b2b760a08c8288251ca26624a78ab3d3
- Size of remote file:
- 431 MB
- SHA256:
- 4e992dfab7211cf67dbab4d804eecb4c6518b48f5ffef702d7f3e7486032b805
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