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:
- cf68c57ff74670373300fea4e022e643a563a9dfe5c19397ed404fc9fb9738b8
- Size of remote file:
- 4.54 kB
- SHA256:
- 3b7ae5fc4fa5f853116aed0be86b23ace9162fb3a57ef9547b493325641f97ed
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.