Instructions to use ModelTC/bert-base-uncased-sst2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ModelTC/bert-base-uncased-sst2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ModelTC/bert-base-uncased-sst2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ModelTC/bert-base-uncased-sst2") model = AutoModelForSequenceClassification.from_pretrained("ModelTC/bert-base-uncased-sst2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5954fa47f0b03307ba7a590a535295a995f40b5ab2ca89a089e4d8fba5dcbf7f
- Size of remote file:
- 438 MB
- SHA256:
- 4a25d8250266ce9d1b7ee05cea4dcae78282d04bc4b1dffe2bb0bd593e588e79
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