Instructions to use allUP/distilbert-annotation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use allUP/distilbert-annotation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="allUP/distilbert-annotation")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("allUP/distilbert-annotation") model = AutoModelForTokenClassification.from_pretrained("allUP/distilbert-annotation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from allUP/distilbert-annotation: direct link, hf CLI and curl.
- Browser
- Download file 265 MB
-
https://huggingface.co/allUP/distilbert-annotation/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://allUP/distilbert-annotation/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/allUP/distilbert-annotation/resolve/main/pytorch_model.bin
265 MB
- Xet hash:
- ed8b9ab4055870c723ccfbd4e6325ce848ea3189ab9a7b869f2f23ae332c3862
- Size of remote file:
- 265 MB
- SHA256:
- f852a47113d7d52c9e3e3b0e079af4a31ea49e26ea5672981ee9c8f6fa01239c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.