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_17-03-32_pjt20230003-aer2024010004-dp-5d9c8bc8b6-8b5n8 /events.out.tfevents.1705478613.pjt20230003-aer2024010004-dp-5d9c8bc8b6-8b5n8.13081.10
- Xet hash:
- a6590dd08667428dab2cb14eb3bc5b7fc1d9cc7311a8a372cb4544c61b8f68f9
- Size of remote file:
- 13 kB
- SHA256:
- 96fa6ad6b4e7c40c292dc0297d69a27b2957783fc9059f90de66f848078880b1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.