Instructions to use selsar/cv_profession with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use selsar/cv_profession with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="selsar/cv_profession", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("selsar/cv_profession") model = AutoModelForSequenceClassification.from_pretrained("selsar/cv_profession", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d8f5fc1f1fcbcab29765d82ea9bfce1931c1c82d4fdd9bb1b6892db0995e1485
- Size of remote file:
- 16.3 MB
- SHA256:
- 0c405b11c22d1def92c599a77ffa8a37b2f7d35654651510aa834cd2d54d5328
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