Instructions to use deetsml/HIMsetfitMultiLabelModel2epochs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deetsml/HIMsetfitMultiLabelModel2epochs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="deetsml/HIMsetfitMultiLabelModel2epochs")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("deetsml/HIMsetfitMultiLabelModel2epochs") model = AutoModel.from_pretrained("deetsml/HIMsetfitMultiLabelModel2epochs", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download 1_Pooling/config.json from deetsml/HIMsetfitMultiLabelModel2epochs: direct link, hf CLI and curl.
- Browser
- Download file 191 Bytes
-
https://huggingface.co/deetsml/HIMsetfitMultiLabelModel2epochs/resolve/main/1_Pooling/config.json
- Command line
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hf download hf://deetsml/HIMsetfitMultiLabelModel2epochs/1_Pooling/config.json
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curl -L -o config.json https://huggingface.co/deetsml/HIMsetfitMultiLabelModel2epochs/resolve/main/1_Pooling/config.json
191 Bytes
| { | |
| "word_embedding_dimension": 1024, | |
| "pooling_mode_cls_token": false, | |
| "pooling_mode_mean_tokens": true, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false | |
| } |