Instructions to use jackfriedson/distilbert-uncased-names with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jackfriedson/distilbert-uncased-names with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="jackfriedson/distilbert-uncased-names")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("jackfriedson/distilbert-uncased-names") model = AutoModelForTokenClassification.from_pretrained("jackfriedson/distilbert-uncased-names", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 18c67054d58c2fdff64cba1b4e744e38cc804302062b788dbdb1b199e7a48eb7
- Size of remote file:
- 4.92 kB
- SHA256:
- 7ba1a36bfc5c853cc415eefea1c1c21b9432f3980045f3a01968f9e601c28509
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