Summarization
Transformers
PyTorch
TensorBoard
t5
text2text-generation
Generated from Trainer
Eval Results (legacy)
text-generation-inference
Instructions to use domenicrosati/t5-small-finetuned-contradiction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use domenicrosati/t5-small-finetuned-contradiction with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="domenicrosati/t5-small-finetuned-contradiction")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("domenicrosati/t5-small-finetuned-contradiction") model = AutoModelForSeq2SeqLM.from_pretrained("domenicrosati/t5-small-finetuned-contradiction") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a2b8bf3410f54425d8a317c8c03b5ac835651ae95c88995d57ad2f169b9a822d
- Size of remote file:
- 3.25 kB
- SHA256:
- 1417c35418255e96add99588456d634cca299cd7ef9b9a470279da040322b48d
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