Instructions to use Datasaur/distilbert-base-uncased-finetuned-conll2003 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Datasaur/distilbert-base-uncased-finetuned-conll2003 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Datasaur/distilbert-base-uncased-finetuned-conll2003")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Datasaur/distilbert-base-uncased-finetuned-conll2003") model = AutoModelForTokenClassification.from_pretrained("Datasaur/distilbert-base-uncased-finetuned-conll2003", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Datasaur/distilbert-base-uncased-finetuned-conll2003: direct link, hf CLI and curl.
- Browser
- Download file 266 MB
-
https://huggingface.co/Datasaur/distilbert-base-uncased-finetuned-conll2003/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Datasaur/distilbert-base-uncased-finetuned-conll2003/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/Datasaur/distilbert-base-uncased-finetuned-conll2003/resolve/main/pytorch_model.bin
266 MB
- Xet hash:
- 4dd70035171e00acf42bbcacb104c26fff112767264547648325aedf1b920f57
- Size of remote file:
- 266 MB
- SHA256:
- 04b24a36d5375dc0609342b30a64221e4d13f5ef8e5218539669b2715c42afb8
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