Instructions to use enelpol/evalatin2022-pos-open with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use enelpol/evalatin2022-pos-open with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="enelpol/evalatin2022-pos-open")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("enelpol/evalatin2022-pos-open") model = AutoModelForTokenClassification.from_pretrained("enelpol/evalatin2022-pos-open", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from enelpol/evalatin2022-pos-open: direct link, hf CLI and curl.
- Browser
- Download file 2.24 GB
-
https://huggingface.co/enelpol/evalatin2022-pos-open/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://enelpol/evalatin2022-pos-open/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/enelpol/evalatin2022-pos-open/resolve/main/pytorch_model.bin
2.24 GB
- Xet hash:
- 39168e31eb750bd0fdf828f0bcb88986b0dc098b2806557cf896dea694429633
- Size of remote file:
- 2.24 GB
- SHA256:
- de8c25e0ad1ba7a2f6cbae21280c46ddbad13ef5e2f3839314ddde79c677e938
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