Token Classification
spaCy
German
job-title-extraction
ner
historical-newspapers
job-advertisements
digital-humanities
Instructions to use Var3n/jobads-position with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use Var3n/jobads-position with spaCy:
!pip install https://huggingface.co/Var3n/jobads-position/resolve/main/jobads-position-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("jobads-position") # Importing as module. import jobads-position nlp = jobads-position.load() - Notebooks
- Google Colab
- Kaggle
Download ner/model from Var3n/jobads-position: direct link, hf CLI and curl.
- Browser
- Download file 207 kB
-
https://huggingface.co/Var3n/jobads-position/resolve/main/ner/model
- Command line
-
hf download hf://Var3n/jobads-position/ner/model
-
curl -L -o model https://huggingface.co/Var3n/jobads-position/resolve/main/ner/model
207 kB
- Xet hash:
- ffc4151aef5f424064819df5fc828f3b5ff2c77b4a36d2d2a68ed9d33a63a13e
- Size of remote file:
- 207 kB
- SHA256:
- 6e9fb272c0a44fdd98262962c783679c80bbcd1498d1e8e4baca2e6ce47ed736
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