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--- |
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license: apache-2.0 |
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base_model: google/long-t5-tglobal-base |
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tags: |
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- summarization |
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- generated_from_trainer |
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datasets: |
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- gov_report_summarization_dataset |
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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-finetuned-govReport-4096 |
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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: gov_report_summarization_dataset |
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type: gov_report_summarization_dataset |
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config: document |
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split: validation |
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args: document |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 0.0432 |
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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-finetuned-govReport-4096 |
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This model is a fine-tuned version of [google/long-t5-tglobal-base](https://huggingface.co/google/long-t5-tglobal-base) on the gov_report_summarization_dataset dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.4052 |
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- Rouge1: 0.0432 |
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- Rouge2: 0.0217 |
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- Rougel: 0.0378 |
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- Rougelsum: 0.0408 |
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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: 10 |
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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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| 15.9484 | 0.99 | 31 | 2.7412 | 0.0382 | 0.0142 | 0.0319 | 0.0354 | |
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| 3.0143 | 1.98 | 62 | 1.7096 | 0.0385 | 0.0144 | 0.032 | 0.0355 | |
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| 2.1893 | 2.98 | 93 | 1.4976 | 0.0376 | 0.0138 | 0.0313 | 0.0347 | |
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| 1.6128 | 4.0 | 125 | 1.4406 | 0.041 | 0.0174 | 0.0354 | 0.0387 | |
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| 1.5438 | 4.99 | 156 | 1.4292 | 0.043 | 0.0203 | 0.0368 | 0.0408 | |
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| 1.5015 | 5.98 | 187 | 1.4220 | 0.0427 | 0.0205 | 0.0367 | 0.0405 | |
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| 1.4723 | 6.98 | 218 | 1.4071 | 0.0431 | 0.0215 | 0.0376 | 0.0408 | |
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| 1.4707 | 8.0 | 250 | 1.4089 | 0.0427 | 0.0212 | 0.0373 | 0.0405 | |
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| 1.4447 | 8.99 | 281 | 1.4046 | 0.0431 | 0.0216 | 0.0379 | 0.0408 | |
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| 1.4884 | 9.92 | 310 | 1.4052 | 0.0432 | 0.0217 | 0.0378 | 0.0408 | |
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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 2.1.0 |
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- Tokenizers 0.15.1 |
