Instructions to use aseifert/distilbert-casing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aseifert/distilbert-casing with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="aseifert/distilbert-casing")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("aseifert/distilbert-casing") model = AutoModelForTokenClassification.from_pretrained("aseifert/distilbert-casing", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 76fe413c7d03e681d2c4b2e7a3ec2f42dc1b46cfb404c778eefd193e6a07c1cc
- Size of remote file:
- 1.47 kB
- SHA256:
- 42ed7e1675052eea8fe9b59aeb309935dbaaeb37adf8b9aec060e1c18c88f4f7
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