Instructions to use TBM99/distilbert-multi-label-conditional-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TBM99/distilbert-multi-label-conditional-Classification with Transformers:
# Load model directly from transformers import AutoTokenizer, DistilBertForConditionalClassification tokenizer = AutoTokenizer.from_pretrained("TBM99/distilbert-multi-label-conditional-Classification") model = DistilBertForConditionalClassification.from_pretrained("TBM99/distilbert-multi-label-conditional-Classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2c9a96485021b3f46ddcf3b3c89f23863addc90ab75698cb80c826df4a9336b7
- Size of remote file:
- 268 MB
- SHA256:
- 3928c69e521a802389ce832494e1da8924255579867d19848b779e8627c69b75
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