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
Commit ·
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update model card README.md
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README.md
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metrics:
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- name: Rouge1
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type: rouge
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value: 34.
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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This model is a fine-tuned version of [domenicrosati/t5-small-finetuned-contradiction](https://huggingface.co/domenicrosati/t5-small-finetuned-contradiction) on the snli dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.
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- Rouge1: 34.
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- Rouge2: 14.
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- Rougel: 32.
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- Rougelsum: 32.
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
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### Framework versions
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metrics:
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- name: Rouge1
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type: rouge
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value: 34.4713
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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This model is a fine-tuned version of [domenicrosati/t5-small-finetuned-contradiction](https://huggingface.co/domenicrosati/t5-small-finetuned-contradiction) on the snli dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.0524
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- Rouge1: 34.4713
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- Rouge2: 14.6253
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- Rougel: 32.5971
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- Rougelsum: 32.5854
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
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| 1.9316 | 1.0 | 2863 | 2.0832 | 34.5611 | 14.5831 | 32.7994 | 32.7887 |
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| 1.9898 | 2.0 | 5726 | 2.0742 | 34.4883 | 14.6727 | 32.6392 | 32.6204 |
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| 1.9995 | 3.0 | 8589 | 2.0658 | 34.6327 | 14.6086 | 32.7722 | 32.7524 |
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| 2.0108 | 4.0 | 11452 | 2.0602 | 34.6013 | 14.6843 | 32.7286 | 32.7192 |
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| 2.0165 | 5.0 | 14315 | 2.0581 | 34.5423 | 14.6649 | 32.705 | 32.6891 |
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| 2.0132 | 6.0 | 17178 | 2.0545 | 34.6902 | 14.7817 | 32.8538 | 32.8374 |
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| 2.0128 | 7.0 | 20041 | 2.0537 | 34.5965 | 14.691 | 32.7323 | 32.7165 |
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| 2.0139 | 8.0 | 22904 | 2.0524 | 34.529 | 14.6524 | 32.6635 | 32.6443 |
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### Framework versions
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