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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 82066f3a0d508e177fc19755c5e10e7bf6db9c9924f42aa9ad432300faef0655
- Size of remote file:
- 627 Bytes
- SHA256:
- 957fe93e7443d50157ecaaf7766fc817c558faceaa8bde7275c4ea14c7fdca60
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