Instructions to use henryscheible/stereoset_binary_bert_finetuned_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use henryscheible/stereoset_binary_bert_finetuned_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="henryscheible/stereoset_binary_bert_finetuned_2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("henryscheible/stereoset_binary_bert_finetuned_2") model = AutoModelForSequenceClassification.from_pretrained("henryscheible/stereoset_binary_bert_finetuned_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from henryscheible/stereoset_binary_bert_finetuned_2: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/henryscheible/stereoset_binary_bert_finetuned_2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://henryscheible/stereoset_binary_bert_finetuned_2/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/henryscheible/stereoset_binary_bert_finetuned_2/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- adf6aa620f441ff51fa93becfbe0104dd7bc405a76a46c5f4d1b9f83f11280c5
- Size of remote file:
- 438 MB
- SHA256:
- 088aa5b21c8ca54dc5d64fca30c5a1b585cc524ae4823ed85013a74f793623d7
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