Instructions to use Aktsvigun/tmp_electra_large_aug_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Aktsvigun/tmp_electra_large_aug_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Aktsvigun/tmp_electra_large_aug_2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Aktsvigun/tmp_electra_large_aug_2") model = AutoModelForSequenceClassification.from_pretrained("Aktsvigun/tmp_electra_large_aug_2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 322d7dd497f7e9c70f456f927b7d48e8871258f4545f8830fffb389e27513e85
- Size of remote file:
- 1.34 GB
- SHA256:
- 136ea77aca460c253dce133c2734bc6b8785e1664a3b4bf3a16939ee6a94f6b9
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