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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_samsum2 |
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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: 0.6112 |
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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_samsum2 |
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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: 1.1949 |
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- Rouge1: 0.6112 |
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- Rouge2: 0.3855 |
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- Rougel: 0.5301 |
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- Rougelsum: 0.5296 |
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- Gen Len: 30.5427 |
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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: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 16 |
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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: 5 |
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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 | 41 | 0.9966 | 0.6323 | 0.416 | 0.5587 | 0.5598 | 26.9573 | |
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| No log | 2.0 | 82 | 1.0976 | 0.6279 | 0.413 | 0.5569 | 0.5583 | 27.8171 | |
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| No log | 3.0 | 123 | 1.1576 | 0.6236 | 0.4141 | 0.553 | 0.5537 | 29.5183 | |
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| No log | 4.0 | 164 | 1.1998 | 0.6148 | 0.3948 | 0.5402 | 0.541 | 30.5061 | |
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| No log | 5.0 | 205 | 1.1949 | 0.6112 | 0.3855 | 0.5301 | 0.5296 | 30.5427 | |
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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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