Instructions to use tiya1012/equality_bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tiya1012/equality_bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tiya1012/equality_bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tiya1012/equality_bert") model = AutoModelForSequenceClassification.from_pretrained("tiya1012/equality_bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from tiya1012/equality_bert: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/tiya1012/equality_bert/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://tiya1012/equality_bert/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/tiya1012/equality_bert/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 0eb58dcc2acb30921aa815538d7b98b907fd06b74b5355ec19c366f2be6764ce
- Size of remote file:
- 438 MB
- SHA256:
- f38d953de5479812d8ea212951288a4e957cf4df41d04146bf1a1736808f6e10
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