Instructions to use Aktsvigun/tmp_electra_large_aug_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Aktsvigun/tmp_electra_large_aug_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Aktsvigun/tmp_electra_large_aug_1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Aktsvigun/tmp_electra_large_aug_1") model = AutoModelForSequenceClassification.from_pretrained("Aktsvigun/tmp_electra_large_aug_1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b2a5eb3606036f83ef2b4fb31805a0bdf879de69b33e626b47317a95641d9f7c
- Size of remote file:
- 1.34 GB
- SHA256:
- a7aa6f4a9630b7ff7cbee91adbc290ec6fd1f2a68942cef9af3fa13ea0325659
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