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_16-52-53_pjt20230003-aer2024010004-dp-5d9c8bc8b6-8b5n8 /events.out.tfevents.1705477974.pjt20230003-aer2024010004-dp-5d9c8bc8b6-8b5n8.13081.8
- Xet hash:
- 20600d8b29ea14b56806d7c9c272ac74c5d2c7cd81108b0b24ff6d1294c6ddf2
- Size of remote file:
- 6.74 kB
- SHA256:
- 5275e5a4aba8429e877d885085db748789a2f713a438c780c14c0b149050a9bc
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