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:
- 5d15c8207b5c7c544dcb8af9d8314e30e4939ab1571de255e8e8481080adec04
- Size of remote file:
- 876 MB
- SHA256:
- f12a84336807ff577babf0cbb83dabb29ad418fb583d6f99146a7fe5247c6dbd
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