Instructions to use selsar/profession with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use selsar/profession with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="selsar/profession")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("selsar/profession") model = AutoModelForSequenceClassification.from_pretrained("selsar/profession", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6f0856b1c8e78abdbcaf91d4d3aeadf6376d3ca18a46127a5cb2bb3ab5630a74
- Size of remote file:
- 1.12 GB
- SHA256:
- 4a9adc7e2f01e739a47162c10b7f8f492a3b4b5ec53bee8be00e0a63cd072ceb
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