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:
- d9e82fb07be074b5cc7f104199caf744e6daaa452aab512c4a7c585d02f9f0c0
- Size of remote file:
- 267 MB
- SHA256:
- 2a2015244ec3c31e7839c9f86399ed411ed5be838b0e891855931819927ac86a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.