Instructions to use tzhao3/Bert-SST2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tzhao3/Bert-SST2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tzhao3/Bert-SST2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tzhao3/Bert-SST2") model = AutoModelForSequenceClassification.from_pretrained("tzhao3/Bert-SST2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from tzhao3/Bert-SST2: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/tzhao3/Bert-SST2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://tzhao3/Bert-SST2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/tzhao3/Bert-SST2/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 6bf566311ec36ef4b4485705793e23fa105cab0bd06cbe5d87b94ddf3640fc15
- Size of remote file:
- 438 MB
- SHA256:
- 98c30a38dd30989be8da164cb4bb3cef3602aa1bbb8d71c59bd31e32bdcf5eb5
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