Instructions to use alicenkbaytop/model_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alicenkbaytop/model_output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="alicenkbaytop/model_output")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("alicenkbaytop/model_output") model = AutoModelForTokenClassification.from_pretrained("alicenkbaytop/model_output", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from alicenkbaytop/model_output: direct link, hf CLI and curl.
- Browser
- Download file 265 MB
-
https://huggingface.co/alicenkbaytop/model_output/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://alicenkbaytop/model_output/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/alicenkbaytop/model_output/resolve/main/pytorch_model.bin
265 MB
- Xet hash:
- 3e4fe09a1b75459390f47c13df0c6701e52c8daa544d16fcfb7d1c6a5007f799
- Size of remote file:
- 265 MB
- SHA256:
- ea284e06a09909440ef5ee386b0921310529974b72d2ffe146fa5e864e3204d8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.