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_16-23-33_pjt20230003-aer2024010004-dp-5d9c8bc8b6-8b5n8 /events.out.tfevents.1705476214.pjt20230003-aer2024010004-dp-5d9c8bc8b6-8b5n8.12848.0
- Xet hash:
- 9ce108f714d0a6d87a818fff0727a454ce58d8352e01335a31021e2d33b04411
- Size of remote file:
- 10.6 kB
- SHA256:
- a2b38a81a223cb63e67bb068ac1f2d9cd51afbaf9d8a38c246e1c4e04e50071a
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