Instructions to use urisoo/distilbert-base-uncased-tokenclassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use urisoo/distilbert-base-uncased-tokenclassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="urisoo/distilbert-base-uncased-tokenclassification")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("urisoo/distilbert-base-uncased-tokenclassification") model = AutoModelForTokenClassification.from_pretrained("urisoo/distilbert-base-uncased-tokenclassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
distilbert-base-uncased-tokenclassification / runs /Jan17_16-03-59_pjt20230003-aer2024010004-dp-5d9c8bc8b6-8b5n8 /events.out.tfevents.1705475040.pjt20230003-aer2024010004-dp-5d9c8bc8b6-8b5n8.12305.0
- Xet hash:
- 2d83318d67a2a1db6f92c9d40d303307d83314aec4b271a8ad3ba1106c323716
- Size of remote file:
- 6.21 kB
- SHA256:
- ba901d6c93bc73d86933f000df11feba4ffb6f82527b716a84b6807f662eae7e
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