Instructions to use muhammadravi251001/tmp_trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use muhammadravi251001/tmp_trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="muhammadravi251001/tmp_trainer")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("muhammadravi251001/tmp_trainer") model = AutoModel.from_pretrained("muhammadravi251001/tmp_trainer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fb995dc84cf0c238bfdd492eeb44c2b776d1850b30924902d0c510818950742b
- Size of remote file:
- 334 MB
- SHA256:
- 84f9dc9cb990f65f5c3a690af57ca3778e7c3176b7dde292a955987f71ddbad1
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