Instructions to use henryscheible/stereoset_binary_bert_all with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use henryscheible/stereoset_binary_bert_all with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="henryscheible/stereoset_binary_bert_all")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("henryscheible/stereoset_binary_bert_all") model = AutoModelForSequenceClassification.from_pretrained("henryscheible/stereoset_binary_bert_all", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from henryscheible/stereoset_binary_bert_all: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/henryscheible/stereoset_binary_bert_all/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://henryscheible/stereoset_binary_bert_all@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/henryscheible/stereoset_binary_bert_all/resolve/refs%2Fpr%2F1/pytorch_model.bin
438 MB
- Xet hash:
- 1ce8be533296027266ea37019e70bcd19678d244f4134ce74b24720b4a6afb39
- Size of remote file:
- 438 MB
- SHA256:
- 44116bfef035d969b0aecf61dce0b1d6694143217e3f5425f77cac180f37a600
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.