Instructions to use intelcomp/nace2_level1_28 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use intelcomp/nace2_level1_28 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="intelcomp/nace2_level1_28")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("intelcomp/nace2_level1_28") model = AutoModelForSequenceClassification.from_pretrained("intelcomp/nace2_level1_28") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5bea4ea9dab4102db865d039bdf434a6a10c876b766a26ece2d2f019b6bea528
- Size of remote file:
- 1.42 GB
- SHA256:
- 24ad3e647c4645dbe97f2cbac3c58cf9c3555828581ff9ee4620d0a9916f614b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.