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:
- 3eddb82e013015010efc56d2a3cb86b3e272d71974cd47f47c8d9c13b9c560b5
- Size of remote file:
- 2.93 kB
- SHA256:
- 903609768ed94e847ce61f0b89aff85f9c0153b7f54de38f5ab9064986c15c44
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