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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: t5-small |
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tags: |
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- generated_from_trainer |
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datasets: |
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- big_patent |
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metrics: |
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- rouge |
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model-index: |
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- name: my_T5_summarization_model |
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results: |
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- task: |
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name: Sequence-to-sequence Language Modeling |
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type: text2text-generation |
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dataset: |
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name: big_patent |
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type: big_patent |
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config: f |
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split: validation |
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args: f |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 0.2277 |
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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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should probably proofread and complete it, then remove this comment. --> |
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# my_T5_summarization_model |
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This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the big_patent dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.9477 |
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- Rouge1: 0.2277 |
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- Rouge2: 0.1286 |
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- Rougel: 0.1988 |
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- Rougelsum: 0.1988 |
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- Gen Len: 19.0 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 4 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| |
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| 2.156 | 1.0 | 5348 | 2.0181 | 0.2264 | 0.1267 | 0.1971 | 0.1972 | 19.0 | |
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| 2.1095 | 2.0 | 10696 | 1.9737 | 0.227 | 0.1276 | 0.1977 | 0.1978 | 19.0 | |
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| 2.0867 | 3.0 | 16044 | 1.9545 | 0.2277 | 0.1285 | 0.1987 | 0.1988 | 19.0 | |
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| 2.0577 | 4.0 | 21392 | 1.9477 | 0.2277 | 0.1286 | 0.1988 | 0.1988 | 19.0 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.5.0+cu121 |
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- Datasets 3.1.0 |
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- Tokenizers 0.19.1 |
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