Instructions to use mekjr1/test_trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mekjr1/test_trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mekjr1/test_trainer")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mekjr1/test_trainer") model = AutoModelForSequenceClassification.from_pretrained("mekjr1/test_trainer") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ed407474fdb593dd7c307efd84f49383a0eb4e2d912151cdf610694721690492
- Size of remote file:
- 433 MB
- SHA256:
- 817f63177085d8433623fedb2741261999a0b3ec2fb033178ccd74019ca7f68c
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