Instructions to use Aktsvigun/tmp_electra_c4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Aktsvigun/tmp_electra_c4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Aktsvigun/tmp_electra_c4")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Aktsvigun/tmp_electra_c4") model = AutoModelForSequenceClassification.from_pretrained("Aktsvigun/tmp_electra_c4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 77c788efb18bcefe70523083fd53fa36bee65a230d8cb59ca93f0a6350c0e6ac
- Size of remote file:
- 1.34 GB
- SHA256:
- 2bbc597e81851390f1740f61e7ddd8fbe1f7536b7402a772f3d942a262cab319
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.