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 training_args.bin from tzhao3/Bert-SST2: direct link, hf CLI and curl.
- Browser
- Download file 3.45 kB
-
https://huggingface.co/tzhao3/Bert-SST2/resolve/main/training_args.bin
- Command line
-
hf download hf://tzhao3/Bert-SST2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/tzhao3/Bert-SST2/resolve/main/training_args.bin
3.45 kB
- Xet hash:
- 1b3d7abbd773cebdfcb0cfdd7b56ab9fccf794368c58a25d8670617841314d3f
- Size of remote file:
- 3.45 kB
- SHA256:
- 3cdd16a8e07f775956ac5ad097fe70122e7ac37f312fc2a2c9292521e96ca8ad
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