Instructions to use Nargizi/screeve-postagger with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nargizi/screeve-postagger with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Nargizi/screeve-postagger")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Nargizi/screeve-postagger") model = AutoModelForTokenClassification.from_pretrained("Nargizi/screeve-postagger", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Nargizi/screeve-postagger: direct link, hf CLI and curl.
- Browser
- Download file 1.73 MB
-
https://huggingface.co/Nargizi/screeve-postagger/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Nargizi/screeve-postagger/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Nargizi/screeve-postagger/resolve/main/pytorch_model.bin
1.73 MB
- Xet hash:
- 0e0ae0ecb20e3e81924faf824b154ce090c8836de4a061da8a1ea1d9645b101d
- Size of remote file:
- 1.73 MB
- SHA256:
- 4f5048328663e43063069416e451bcba04287c318432a53ec15ee273b55e690c
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