Instructions to use selsar/labor_market_position with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use selsar/labor_market_position with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="selsar/labor_market_position")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("selsar/labor_market_position") model = AutoModelForSequenceClassification.from_pretrained("selsar/labor_market_position", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from selsar/labor_market_position: direct link, hf CLI and curl.
- Browser
- Download file 16.3 MB
-
https://huggingface.co/selsar/labor_market_position/resolve/main/tokenizer.json
- Command line
-
hf download hf://selsar/labor_market_position/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/selsar/labor_market_position/resolve/main/tokenizer.json
16.3 MB
- Xet hash:
- 64b05138a1f39e1de43b9fd58d396a667ba1503d276e5127ccc1353695aa85d5
- Size of remote file:
- 16.3 MB
- SHA256:
- 271a2e08979ac1e5ba613ece98fc0be70a21ed61ca3db888037c3e137b1ac69e
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