Instructions to use Fsoft-AIC/dopamin-python-summary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Fsoft-AIC/dopamin-python-summary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Fsoft-AIC/dopamin-python-summary")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Fsoft-AIC/dopamin-python-summary") model = AutoModelForSequenceClassification.from_pretrained("Fsoft-AIC/dopamin-python-summary") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2631a194b228bef4ea895321928fd7d581cf44ecf131a59f51c2423232711945
- Size of remote file:
- 612 MB
- SHA256:
- b6bb13ec47d62a44194d2968eb2bef25ed055207acad4db3117813cc3b1b5124
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