Instructions to use henryscheible/stereoset_binary_bert_finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use henryscheible/stereoset_binary_bert_finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="henryscheible/stereoset_binary_bert_finetuned")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("henryscheible/stereoset_binary_bert_finetuned") model = AutoModelForSequenceClassification.from_pretrained("henryscheible/stereoset_binary_bert_finetuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from henryscheible/stereoset_binary_bert_finetuned: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/henryscheible/stereoset_binary_bert_finetuned/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://henryscheible/stereoset_binary_bert_finetuned/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/henryscheible/stereoset_binary_bert_finetuned/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 079bc99c2e5a15cd5996e4d2eefc0782024b637d584a944417f4f9427c1bd488
- Size of remote file:
- 438 MB
- SHA256:
- 7429ed8fb903df3e0d8c0f9700eff76161543748c8fa15444c5a7caf3d56640f
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