Instructions to use szymonpindur/herbert-onom-interjection-recognition with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use szymonpindur/herbert-onom-interjection-recognition with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="szymonpindur/herbert-onom-interjection-recognition")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("szymonpindur/herbert-onom-interjection-recognition") model = AutoModelForTokenClassification.from_pretrained("szymonpindur/herbert-onom-interjection-recognition", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from szymonpindur/herbert-onom-interjection-recognition: direct link, hf CLI and curl.
- Browser
- Download file 1.42 GB
-
https://huggingface.co/szymonpindur/herbert-onom-interjection-recognition/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://szymonpindur/herbert-onom-interjection-recognition/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/szymonpindur/herbert-onom-interjection-recognition/resolve/main/pytorch_model.bin
1.42 GB
- Xet hash:
- 535932359f52048a517a9e39dd70b5c4f2654992b36d32411b8826584e8f45ae
- Size of remote file:
- 1.42 GB
- SHA256:
- fd8293ab6fcbc9d512d025d0eb95b38c1772f21a7432d9e1ced24cdfbbb3259e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.