Instructions to use tzhao3/Bert-M-SST2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tzhao3/Bert-M-SST2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tzhao3/Bert-M-SST2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tzhao3/Bert-M-SST2") model = AutoModelForSequenceClassification.from_pretrained("tzhao3/Bert-M-SST2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 736d0c93114a8a1d15845d2ce2a3ade2ceee2a9053912885908167f6d7c5ae2d
- Size of remote file:
- 627 Bytes
- SHA256:
- 466f0b6297493eed05f2d1ce63809b3d054e71f229177cc77584e4462fd9dace
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