Instructions to use huggingfans/electraL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use huggingfans/electraL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="huggingfans/electraL")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("huggingfans/electraL") model = AutoModelForSequenceClassification.from_pretrained("huggingfans/electraL", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from huggingfans/electraL: direct link, hf CLI and curl.
- Browser
- Download file 70.1 MB
-
https://huggingface.co/huggingfans/electraL/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://huggingfans/electraL/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/huggingfans/electraL/resolve/main/pytorch_model.bin
70.1 MB
- Xet hash:
- 86531f8fd0f571b1a518717baedad5c0801e55f3018a06a90c8b60102b4e1c78
- Size of remote file:
- 70.1 MB
- SHA256:
- aa9c672751365cea9d0efbc1e9f7eba062a26efcd938b2adfe5037392bc0b0d5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.