Instructions to use maple/distilbert-base-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use maple/distilbert-base-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="maple/distilbert-base-cased")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("maple/distilbert-base-cased") model = AutoModelForTokenClassification.from_pretrained("maple/distilbert-base-cased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fada85c1034430ca8fd9013aa7f68fff0f7dd5217d72f08a2562783a9121a1d2
- Size of remote file:
- 261 MB
- SHA256:
- d1dafd473ac57b08730ef54d4e4b1cbe1052cde212d05f0214fc0e21ae33d610
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