Instructions to use Aktsvigun/tmp_electra_large_aug_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Aktsvigun/tmp_electra_large_aug_3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Aktsvigun/tmp_electra_large_aug_3")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Aktsvigun/tmp_electra_large_aug_3") model = AutoModelForSequenceClassification.from_pretrained("Aktsvigun/tmp_electra_large_aug_3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Aktsvigun/tmp_electra_large_aug_3: direct link, hf CLI and curl.
- Browser
- Download file 1.34 GB
-
https://huggingface.co/Aktsvigun/tmp_electra_large_aug_3/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Aktsvigun/tmp_electra_large_aug_3/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Aktsvigun/tmp_electra_large_aug_3/resolve/main/pytorch_model.bin
1.34 GB
- Xet hash:
- 5ed0333c9c751292528411e2ab4aa343f516ce64744b7d392ef8a8302a229b9d
- Size of remote file:
- 1.34 GB
- SHA256:
- d22ffb9bc3b3f6d07b3dfda20f67f15249b9353485ac6d2ded51e57553455789
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