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--- |
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license: apache-2.0 |
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base_model: RMWeerasinghe/long-t5-tglobal-base-finetuned-govReport-4096 |
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tags: |
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- summarization |
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- generated_from_trainer |
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metrics: |
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- rouge |
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model-index: |
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- name: long-t5-tglobal-base-boardpapers-4096 |
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results: [] |
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pipeline_tag: summarization |
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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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# long-t5-tglobal-base-boardpapers-4096 |
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This model is a fine-tuned version of [RMWeerasinghe/long-t5-tglobal-base-finetuned-govReport-4096](https://huggingface.co/RMWeerasinghe/long-t5-tglobal-base-finetuned-govReport-4096) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5617 |
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- Rouge1: 0.0743 |
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- Rouge2: 0.0398 |
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- Rougel: 0.0589 |
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- Rougelsum: 0.0703 |
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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: 4e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 32 |
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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: 30 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| |
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| No log | 0.67 | 1 | 0.6654 | 0.0514 | 0.0197 | 0.0386 | 0.0477 | |
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| No log | 2.0 | 3 | 0.6378 | 0.0667 | 0.0309 | 0.0512 | 0.0596 | |
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| No log | 2.67 | 4 | 0.6293 | 0.0646 | 0.0274 | 0.0515 | 0.0619 | |
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| No log | 4.0 | 6 | 0.6128 | 0.0706 | 0.0377 | 0.0566 | 0.067 | |
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| No log | 4.67 | 7 | 0.6049 | 0.0706 | 0.0377 | 0.0566 | 0.067 | |
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| No log | 6.0 | 9 | 0.5935 | 0.0706 | 0.0377 | 0.0566 | 0.067 | |
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| No log | 6.67 | 10 | 0.5891 | 0.0718 | 0.0385 | 0.0578 | 0.067 | |
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| No log | 8.0 | 12 | 0.5815 | 0.0743 | 0.0398 | 0.0589 | 0.0703 | |
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| No log | 8.67 | 13 | 0.5785 | 0.0743 | 0.0398 | 0.0589 | 0.0703 | |
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| No log | 10.0 | 15 | 0.5742 | 0.0743 | 0.0398 | 0.0589 | 0.0703 | |
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| No log | 10.67 | 16 | 0.5724 | 0.0743 | 0.0398 | 0.0589 | 0.0703 | |
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| No log | 12.0 | 18 | 0.5694 | 0.0743 | 0.0398 | 0.0589 | 0.0703 | |
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| No log | 12.67 | 19 | 0.5681 | 0.0743 | 0.0398 | 0.0589 | 0.0703 | |
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| 0.7929 | 14.0 | 21 | 0.5661 | 0.0743 | 0.0398 | 0.0589 | 0.0703 | |
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| 0.7929 | 14.67 | 22 | 0.5652 | 0.0743 | 0.0398 | 0.0589 | 0.0703 | |
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| 0.7929 | 16.0 | 24 | 0.5636 | 0.0743 | 0.0398 | 0.0589 | 0.0703 | |
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| 0.7929 | 16.67 | 25 | 0.5630 | 0.0743 | 0.0398 | 0.0589 | 0.0703 | |
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| 0.7929 | 18.0 | 27 | 0.5621 | 0.0743 | 0.0398 | 0.0589 | 0.0703 | |
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| 0.7929 | 18.67 | 28 | 0.5619 | 0.0743 | 0.0398 | 0.0589 | 0.0703 | |
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| 0.7929 | 20.0 | 30 | 0.5617 | 0.0743 | 0.0398 | 0.0589 | 0.0703 | |
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### Framework versions |
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- Transformers 4.37.0 |
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- Pytorch 2.1.2 |
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- Datasets 2.17.0 |
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- Tokenizers 0.15.1 |
