Instructions to use tiya1012/equality_albert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tiya1012/equality_albert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tiya1012/equality_albert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tiya1012/equality_albert") model = AutoModelForSequenceClassification.from_pretrained("tiya1012/equality_albert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from tiya1012/equality_albert: direct link, hf CLI and curl.
- Browser
- Download file 46.8 MB
-
https://huggingface.co/tiya1012/equality_albert/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://tiya1012/equality_albert@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/tiya1012/equality_albert/resolve/refs%2Fpr%2F1/pytorch_model.bin
46.8 MB
- Xet hash:
- 5d60015dde70ad49f960e453344ee082d742651f5da9b426b57c04f2cec6c1a1
- Size of remote file:
- 46.8 MB
- SHA256:
- ab4d083ef9997e3b35f79113639c58d5348dcd7cad2176ba0309e2e7a29ef1d1
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