Instructions to use bnabin/tmp_trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bnabin/tmp_trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bnabin/tmp_trainer")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bnabin/tmp_trainer") model = AutoModelForSequenceClassification.from_pretrained("bnabin/tmp_trainer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ea86fd3eea26e7ecf86cf5cba58f812047a4881c7475ca3b796711bcf0f5f0a0
- Size of remote file:
- 438 MB
- SHA256:
- 8edb91f6c6323ad09b838d6008820eed38a08e52b4c35aeb119cb28d48c7df35
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