Instructions to use ba-claim/electra with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ba-claim/electra with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ba-claim/electra")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ba-claim/electra") model = AutoModelForSequenceClassification.from_pretrained("ba-claim/electra", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ba-claim/electra: direct link, hf CLI and curl.
- Browser
- Download file 134 MB
-
https://huggingface.co/ba-claim/electra/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ba-claim/electra/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ba-claim/electra/resolve/main/pytorch_model.bin
134 MB
- Xet hash:
- 5fcc2300c967ca539f04ad7492c3eb447994ca4541d80493ad5a71a221a43c1d
- Size of remote file:
- 134 MB
- SHA256:
- 7b31d92657e52482964482c208e7f533d264fa6bd2622ffbfcffa4529bafd940
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