Instructions to use pmthangk09/bert-base-uncased-sst with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pmthangk09/bert-base-uncased-sst with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pmthangk09/bert-base-uncased-sst")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pmthangk09/bert-base-uncased-sst") model = AutoModelForSequenceClassification.from_pretrained("pmthangk09/bert-base-uncased-sst") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 011a2fd670e113aee140f39dc38d0d9ddca3a1700284b791d8cb7a92d7dd5780
- Size of remote file:
- 438 MB
- SHA256:
- 44a020a79cb30f3737947c08104635f370070d8defac93dd40a7380703e5b935
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