Instructions to use selsar/cv_profession_v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use selsar/cv_profession_v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="selsar/cv_profession_v3")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("selsar/cv_profession_v3") model = AutoModelForSequenceClassification.from_pretrained("selsar/cv_profession_v3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from selsar/cv_profession_v3: direct link, hf CLI and curl.
- Browser
- Download file 16 MB
-
https://huggingface.co/selsar/cv_profession_v3/resolve/main/tokenizer.json
- Command line
-
hf download hf://selsar/cv_profession_v3/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/selsar/cv_profession_v3/resolve/main/tokenizer.json
16 MB
- Xet hash:
- 4769686f0ce47acecd3d9120759df4c2baeb1b5572af61d85dc8a151a5552694
- Size of remote file:
- 16 MB
- SHA256:
- a43a63895b18495744e1f257fa161958c578612ec67b8e0b20d24d93cbcc56b9
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