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--- |
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license: mit |
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base_model: alexdg19/bert_large_xsum_samsum |
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tags: |
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- generated_from_trainer |
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datasets: |
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- samsum |
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metrics: |
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- rouge |
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model-index: |
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- name: bert_large_xsum_samsum3 |
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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: samsum |
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type: samsum |
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config: samsum |
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split: test |
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args: samsum |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 0.5313 |
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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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# bert_large_xsum_samsum3 |
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This model is a fine-tuned version of [alexdg19/bert_large_xsum_samsum](https://huggingface.co/alexdg19/bert_large_xsum_samsum) on the samsum dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.2354 |
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- Rouge1: 0.5313 |
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- Rouge2: 0.2827 |
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- Rougel: 0.4367 |
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- Rougelsum: 0.4357 |
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- Gen Len: 30.939 |
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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: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 4 |
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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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- 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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| No log | 1.0 | 164 | 1.1370 | 0.5599 | 0.3246 | 0.4748 | 0.4743 | 29.0122 | |
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| No log | 2.0 | 328 | 1.2659 | 0.5494 | 0.3033 | 0.4623 | 0.4612 | 27.0671 | |
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| No log | 3.0 | 492 | 1.4188 | 0.5198 | 0.2726 | 0.436 | 0.4346 | 28.6768 | |
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| 0.6603 | 4.0 | 656 | 1.5628 | 0.5391 | 0.2905 | 0.4555 | 0.4553 | 28.6159 | |
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| 0.6603 | 5.0 | 820 | 1.9045 | 0.5237 | 0.2774 | 0.4326 | 0.4321 | 31.5854 | |
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| 0.6603 | 6.0 | 984 | 2.0670 | 0.5199 | 0.2689 | 0.4251 | 0.4243 | 31.8049 | |
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| 0.1722 | 7.0 | 1148 | 1.9653 | 0.5269 | 0.2703 | 0.4342 | 0.4333 | 28.5122 | |
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| 0.1722 | 8.0 | 1312 | 2.1921 | 0.5296 | 0.2765 | 0.4393 | 0.4387 | 31.8354 | |
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| 0.1722 | 9.0 | 1476 | 2.4336 | 0.5299 | 0.2825 | 0.4399 | 0.4388 | 31.7988 | |
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| 0.052 | 10.0 | 1640 | 2.2354 | 0.5313 | 0.2827 | 0.4367 | 0.4357 | 30.939 | |
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### Framework versions |
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- Transformers 4.35.0 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
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