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--- |
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license: mit |
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base_model: facebook/bart-large-xsum |
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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: bart-samsum |
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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: validation |
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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: 0.547 |
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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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# bart-samsum |
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This model is a fine-tuned version of [facebook/bart-large-xsum](https://huggingface.co/facebook/bart-large-xsum) on the samsum dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.3852 |
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- Rouge1: 0.547 |
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- Rouge2: 0.2837 |
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- Rougel: 0.4462 |
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- Rougelsum: 0.4454 |
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- Gen Len: 29.72 |
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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: 2 |
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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: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 3 |
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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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| 1.5201 | 0.27 | 500 | 1.4589 | 0.5276 | 0.2694 | 0.4246 | 0.424 | 33.5067 | |
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| 1.3757 | 0.54 | 1000 | 1.5105 | 0.506 | 0.2566 | 0.415 | 0.4146 | 29.76 | |
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| 1.3496 | 0.81 | 1500 | 1.4039 | 0.5365 | 0.2759 | 0.4233 | 0.4221 | 29.8 | |
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| 1.094 | 1.09 | 2000 | 1.4119 | 0.5407 | 0.2827 | 0.4293 | 0.4288 | 29.84 | |
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| 1.1488 | 1.36 | 2500 | 1.3680 | 0.5275 | 0.2637 | 0.423 | 0.4224 | 26.92 | |
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| 1.1222 | 1.63 | 3000 | 1.2875 | 0.5369 | 0.2844 | 0.4473 | 0.4463 | 29.2267 | |
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| 1.1092 | 1.9 | 3500 | 1.3968 | 0.533 | 0.2818 | 0.4354 | 0.4363 | 30.0667 | |
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| 0.8509 | 2.17 | 4000 | 1.3682 | 0.5306 | 0.2874 | 0.4327 | 0.4331 | 29.1467 | |
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| 0.9565 | 2.44 | 4500 | 1.3450 | 0.5466 | 0.2782 | 0.4419 | 0.4409 | 29.2133 | |
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| 0.8496 | 2.72 | 5000 | 1.3768 | 0.5366 | 0.2807 | 0.4359 | 0.4351 | 30.7733 | |
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| 0.8397 | 2.99 | 5500 | 1.3852 | 0.547 | 0.2837 | 0.4462 | 0.4454 | 29.72 | |
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### Framework versions |
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- Transformers 4.33.1 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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