Instructions to use universalner/uner_swe_tal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use universalner/uner_swe_tal with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="universalner/uner_swe_tal")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("universalner/uner_swe_tal") model = AutoModelForTokenClassification.from_pretrained("universalner/uner_swe_tal", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from universalner/uner_swe_tal: direct link, hf CLI and curl.
- Browser
- Download file 2.24 GB
-
https://huggingface.co/universalner/uner_swe_tal/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://universalner/uner_swe_tal@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/universalner/uner_swe_tal/resolve/refs%2Fpr%2F1/pytorch_model.bin
2.24 GB
- Xet hash:
- a9a0e70e87fa3ee62c51e6637c9489e2c3374384b06a60747b8a8d03b22697ad
- Size of remote file:
- 2.24 GB
- SHA256:
- 4b9399a384a30880330448178ceb8118e2c3f31682c278df1e31c140f54629cc
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