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--- |
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tags: |
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- generated_from_trainer |
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metrics: |
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- rouge |
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license: apache-2.0 |
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datasets: |
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- pszemraj/qmsum-cleaned |
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language: |
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- en |
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pipeline_tag: summarization |
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inference: false |
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--- |
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# long-t5-tglobal-xl-qmsum-wip |
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> ⚠️ warning - this is a work in progress ⚠️ |
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<a href="https://colab.research.google.com/gist/pszemraj/ea0ac20dae4ad84bea4ea64543f84a85/long-t5-tglobal-xl-qmsum-wip.ipynb"> |
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<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> |
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</a> |
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This model is a fine-tuned version of [google/long-t5-tglobal-xl](https://huggingface.co/google/long-t5-tglobal-xl) on the `pszemraj/qmsum-cleaned` dataset. |
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- Refer to the [dataset card](https://huggingface.co/datasets/pszemraj/qmsum-cleaned) for details but this model was trained **with the task/prompt prefixes at the start of `input`** which means that **inference should be run in a similar fashion**. |
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- an example of how to run inference is in the Colab notebook linked above. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.0505 |
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- Rouge1: 35.3881 |
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- Rouge2: 11.509 |
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- Rougel: 23.1543 |
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- Rougelsum: 31.3295 |
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- Gen Len: 80.8 |
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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: 7e-05 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 2526 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 8 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.03 |
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- num_epochs: 3.0 |
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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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| 1.5376 | 1.0 | 99 | 2.0104 | 35.8802 | 11.4595 | 23.6656 | 31.49 | 77.77 | |
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| 1.499 | 2.0 | 198 | 2.0358 | 35.1265 | 11.549 | 23.1062 | 30.8815 | 88.88 | |
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| 1.5034 | 3.0 | 297 | 2.0505 | 35.3881 | 11.509 | 23.1543 | 31.3295 | 80.8 | |
