Instructions to use Aktsvigun/tmp_electra_large_aug_4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Aktsvigun/tmp_electra_large_aug_4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Aktsvigun/tmp_electra_large_aug_4")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Aktsvigun/tmp_electra_large_aug_4") model = AutoModelForSequenceClassification.from_pretrained("Aktsvigun/tmp_electra_large_aug_4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Aktsvigun/tmp_electra_large_aug_4: direct link, hf CLI and curl.
- Browser
- Download file 1.34 GB
-
https://huggingface.co/Aktsvigun/tmp_electra_large_aug_4/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://Aktsvigun/tmp_electra_large_aug_4@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Aktsvigun/tmp_electra_large_aug_4/resolve/refs%2Fpr%2F1/pytorch_model.bin
1.34 GB
- Xet hash:
- bf91c32b23e7f7ee928d20217b1a6595c0bc1b9c8b082600198bb3b79249bb8f
- Size of remote file:
- 1.34 GB
- SHA256:
- 597790cefeb2a6579775cc2e578d245158fac99e21da169df06c5c6874691fd4
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