Instructions to use selsar/cv_profession_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use selsar/cv_profession_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="selsar/cv_profession_v2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("selsar/cv_profession_v2") model = AutoModelForSequenceClassification.from_pretrained("selsar/cv_profession_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from selsar/cv_profession_v2: direct link, hf CLI and curl.
- Browser
- Download file 16.3 MB
-
https://huggingface.co/selsar/cv_profession_v2/resolve/main/tokenizer.json
- Command line
-
hf download hf://selsar/cv_profession_v2/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/selsar/cv_profession_v2/resolve/main/tokenizer.json
16.3 MB
- Xet hash:
- d8f5fc1f1fcbcab29765d82ea9bfce1931c1c82d4fdd9bb1b6892db0995e1485
- Size of remote file:
- 16.3 MB
- SHA256:
- 0c405b11c22d1def92c599a77ffa8a37b2f7d35654651510aa834cd2d54d5328
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