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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: 0.0463 |
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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: 1.3116 |
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- Rouge1: 0.0463 |
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- Rouge2: 0.0231 |
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- Rougel: 0.039 |
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- Rougelsum: 0.0436 |
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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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| 14.9489 | 1.0 | 25 | 2.8019 | 0.0416 | 0.0156 | 0.0343 | 0.0383 | |
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| 3.9751 | 2.0 | 50 | 2.0899 | 0.0396 | 0.0145 | 0.0332 | 0.0367 | |
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| 3.0361 | 3.0 | 75 | 1.4337 | 0.0395 | 0.0138 | 0.033 | 0.0366 | |
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| 1.8477 | 4.0 | 100 | 1.3654 | 0.0406 | 0.0157 | 0.0342 | 0.0378 | |
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| 1.6709 | 5.0 | 125 | 1.3355 | 0.0419 | 0.0171 | 0.036 | 0.0394 | |
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| 1.5673 | 6.0 | 150 | 1.3229 | 0.0446 | 0.022 | 0.0383 | 0.0423 | |
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| 1.5567 | 7.0 | 175 | 1.3181 | 0.047 | 0.0233 | 0.0393 | 0.0441 | |
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| 1.5066 | 8.0 | 200 | 1.3135 | 0.0467 | 0.0233 | 0.0394 | 0.0441 | |
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| 1.515 | 9.0 | 225 | 1.3117 | 0.0462 | 0.0231 | 0.0389 | 0.0436 | |
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| 1.4868 | 10.0 | 250 | 1.3116 | 0.0463 | 0.0231 | 0.039 | 0.0436 | |
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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 |
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