Instructions to use selsar/nli-multilabel-professions-new with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use selsar/nli-multilabel-professions-new with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="selsar/nli-multilabel-professions-new")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("selsar/nli-multilabel-professions-new") model = AutoModelForSequenceClassification.from_pretrained("selsar/nli-multilabel-professions-new", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from selsar/nli-multilabel-professions-new: direct link, hf CLI and curl.
- Browser
- Download file 16.3 MB
-
https://huggingface.co/selsar/nli-multilabel-professions-new/resolve/main/tokenizer.json
- Command line
-
hf download hf://selsar/nli-multilabel-professions-new/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/selsar/nli-multilabel-professions-new/resolve/main/tokenizer.json
16.3 MB
- Xet hash:
- 96ac542866e3d0b96b37128f3a76d1ce0e45860decde998e41c3ef3e58894da0
- Size of remote file:
- 16.3 MB
- SHA256:
- 824e7d82bc5560a30dcf36d84d4598ddcb0ca3c1b3fe38e698105f778bf89d3e
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