Instructions to use intelcomp/nace2_level1_29 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use intelcomp/nace2_level1_29 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="intelcomp/nace2_level1_29")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("intelcomp/nace2_level1_29") model = AutoModelForSequenceClassification.from_pretrained("intelcomp/nace2_level1_29", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4abf4b4a5f0d3cfc08e644b1c652b16d465b98c7a3ef960a7cdfa26e8463635a
- Size of remote file:
- 1.42 GB
- SHA256:
- 0291d671a6babc8601c0a7b7735bb48dd4f079974d63b2b03f534535f9fcf6ac
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