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_15-46-31_pjt20230003-aer2024010004-dp-5d9c8bc8b6-8b5n8 /events.out.tfevents.1705473992.pjt20230003-aer2024010004-dp-5d9c8bc8b6-8b5n8.11792.0
- Xet hash:
- 15317e9a2dce1f14db43779673f01bb4516c3fc385234b3b8c2f2daf23a4c145
- Size of remote file:
- 5.39 kB
- SHA256:
- 193bcde0564c9b7e7999b91aa5c36dafdc18b4c339d5e9a481f017a668d937f1
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