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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 654894b8a162f4a00d322e01ca68703fb384ccc7041ae8067ff49256e5a17c2b
- Size of remote file:
- 3.45 kB
- SHA256:
- 4557ffcc4c6dad719e498069768f07a7614b84717787da44cab7a86c8f2c67ce
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