Instructions to use henryscheible/stereoset_binary_bert_classifieronly_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use henryscheible/stereoset_binary_bert_classifieronly_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="henryscheible/stereoset_binary_bert_classifieronly_1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("henryscheible/stereoset_binary_bert_classifieronly_1") model = AutoModelForSequenceClassification.from_pretrained("henryscheible/stereoset_binary_bert_classifieronly_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from henryscheible/stereoset_binary_bert_classifieronly_1: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/henryscheible/stereoset_binary_bert_classifieronly_1/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://henryscheible/stereoset_binary_bert_classifieronly_1@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/henryscheible/stereoset_binary_bert_classifieronly_1/resolve/refs%2Fpr%2F1/pytorch_model.bin
438 MB
- Xet hash:
- dd8fe9f602ff95102f554f8b0f8be6e77659aeaa4d2442cfa238a5ca395ebab2
- Size of remote file:
- 438 MB
- SHA256:
- f0961ef5c15fab7ede01e98abb28fe6eab32c223e8510009a3d93c0d00220e57
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.