Instructions to use shiv2050/test_trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shiv2050/test_trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="shiv2050/test_trainer")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("shiv2050/test_trainer") model = AutoModelForSequenceClassification.from_pretrained("shiv2050/test_trainer") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 72d6aa9f0114a0bf7217ed37c70f71762f833cf1fb4e97f70b034c6b16f0d013
- Size of remote file:
- 4.92 kB
- SHA256:
- f21f31013f7b11e42a5d384aee30939f001c1c67e5d06b4f5c7c62a3adc7e6d5
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