Instructions to use MaiiaCompsolutions/multiclass_ind_clf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MaiiaCompsolutions/multiclass_ind_clf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MaiiaCompsolutions/multiclass_ind_clf")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("MaiiaCompsolutions/multiclass_ind_clf") model = AutoModel.from_pretrained("MaiiaCompsolutions/multiclass_ind_clf", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from MaiiaCompsolutions/multiclass_ind_clf: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/MaiiaCompsolutions/multiclass_ind_clf/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://MaiiaCompsolutions/multiclass_ind_clf/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/MaiiaCompsolutions/multiclass_ind_clf/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- fc04a28aa5262eb676989e69d0692529db579d740c8e3852c78451f3697c8aef
- Size of remote file:
- 438 MB
- SHA256:
- 29052efb79076094bc5c65c98f9f757b92b05e3201546edd35256ecfae87a0b3
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