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:
- a3e6864678300a25ef231d746b2d018dd8df4f864ea1eb4c46a300215688cfd7
- Size of remote file:
- 3.58 kB
- SHA256:
- 9cd7a442b46d17a676248c68dba1fdc0664146188d701018b33e2aa45f225bd6
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