Instructions to use jacksontran/bert-base-uncased-conll2003 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jacksontran/bert-base-uncased-conll2003 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="jacksontran/bert-base-uncased-conll2003")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("jacksontran/bert-base-uncased-conll2003") model = AutoModelForTokenClassification.from_pretrained("jacksontran/bert-base-uncased-conll2003", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jacksontran/bert-base-uncased-conll2003: direct link, hf CLI and curl.
- Browser
- Download file 436 MB
-
https://huggingface.co/jacksontran/bert-base-uncased-conll2003/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jacksontran/bert-base-uncased-conll2003/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jacksontran/bert-base-uncased-conll2003/resolve/main/pytorch_model.bin
436 MB
- Xet hash:
- 8c643ac8a4fbd73523ddcbc48fba80bf3b831ae61e0b211ef6da167b88ab1a8d
- Size of remote file:
- 436 MB
- SHA256:
- 4a1feeb093f17d678929a10cd325f2bc00372ce9e89a1ab916b72d9973500aea
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