Instructions to use tiya1012/equality_distilbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tiya1012/equality_distilbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tiya1012/equality_distilbert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tiya1012/equality_distilbert") model = AutoModelForSequenceClassification.from_pretrained("tiya1012/equality_distilbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from tiya1012/equality_distilbert: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/tiya1012/equality_distilbert/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://tiya1012/equality_distilbert/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/tiya1012/equality_distilbert/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- c4e391b99553b014800df7f13331b9420ec5924f77315fae1e7e4cf7e4ecad5a
- Size of remote file:
- 268 MB
- SHA256:
- 4b445bb3d05ea8091f15293903a897580a83e569e706c493f11d2804d0a886fb
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