Instructions to use bisoye/distilbert-base-cased_token_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bisoye/distilbert-base-cased_token_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="bisoye/distilbert-base-cased_token_classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("bisoye/distilbert-base-cased_token_classification") model = AutoModelForTokenClassification.from_pretrained("bisoye/distilbert-base-cased_token_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from bisoye/distilbert-base-cased_token_classification: direct link, hf CLI and curl.
- Browser
- Download file 5.24 kB
-
https://huggingface.co/bisoye/distilbert-base-cased_token_classification/resolve/main/training_args.bin
- Command line
-
hf download hf://bisoye/distilbert-base-cased_token_classification/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/bisoye/distilbert-base-cased_token_classification/resolve/main/training_args.bin
5.24 kB
- Xet hash:
- 191dd31c17dd3f19933b3c659f668c959db944f474c2e58d23e07707c938708a
- Size of remote file:
- 5.24 kB
- SHA256:
- 6cda275d13ecc7caa55eea6d5e341251f77feac73ca091597a32a4da769cb8db
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