Instructions to use rach405/test_trainer5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rach405/test_trainer5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rach405/test_trainer5")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rach405/test_trainer5") model = AutoModelForSequenceClassification.from_pretrained("rach405/test_trainer5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f287ebebbc83c69adcc70f41659f5ea079c0ce3aa7437061a10292d3d88dff4a
- Size of remote file:
- 433 MB
- SHA256:
- 1d048fcd66b21f2177db15c6c4cdbbaf05cf44b5ae99fc11f127945f8d9e832e
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