Instructions to use Aktsvigun/tmp_electra_base_aug with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Aktsvigun/tmp_electra_base_aug with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Aktsvigun/tmp_electra_base_aug")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Aktsvigun/tmp_electra_base_aug") model = AutoModelForSequenceClassification.from_pretrained("Aktsvigun/tmp_electra_base_aug", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d14c27ef42d27b21e53eeb5da03f07e8a7f66ddde531a4e1b6b9d2e1c7da17c0
- Size of remote file:
- 438 MB
- SHA256:
- ec8720831729698cd04f71b69ed5dd56727ccabc4cbde9be9499b87d4208edf1
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