Instructions to use tiya1012/crossde_electra with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tiya1012/crossde_electra with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tiya1012/crossde_electra")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tiya1012/crossde_electra") model = AutoModelForSequenceClassification.from_pretrained("tiya1012/crossde_electra", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from tiya1012/crossde_electra: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/tiya1012/crossde_electra/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://tiya1012/crossde_electra/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/tiya1012/crossde_electra/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 7d34dc1cd7d1bcbb6c9b08e67b381ec0728ce65504dacea456458ed193644827
- Size of remote file:
- 438 MB
- SHA256:
- 84ea4ce03dd96014585536f4714542bb59c114f30f51c9e9429439b60f85c485
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.