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--- |
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license: apache-2.0 |
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base_model: sshleifer/distilbart-xsum-12-6 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: bart-abs-1509-0313-lr-0.0003-bs-2-maxep-6 |
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results: [] |
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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-abs-1509-0313-lr-0.0003-bs-2-maxep-6 |
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This model is a fine-tuned version of [sshleifer/distilbart-xsum-12-6](https://huggingface.co/sshleifer/distilbart-xsum-12-6) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 6.8567 |
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- Rouge/rouge1: 0.3035 |
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- Rouge/rouge2: 0.072 |
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- Rouge/rougel: 0.2428 |
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- Rouge/rougelsum: 0.2429 |
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- Bertscore/bertscore-precision: 0.8724 |
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- Bertscore/bertscore-recall: 0.8571 |
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- Bertscore/bertscore-f1: 0.8646 |
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- Meteor: 0.2108 |
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- Gen Len: 29.0 |
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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: 0.0003 |
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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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- 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: 6 |
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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 | Rouge/rouge1 | Rouge/rouge2 | Rouge/rougel | Rouge/rougelsum | Bertscore/bertscore-precision | Bertscore/bertscore-recall | Bertscore/bertscore-f1 | Meteor | Gen Len | |
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|:-------------:|:-----:|:----:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:------:|:-------:| |
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| 2.4741 | 1.0 | 434 | 4.0269 | 0.2771 | 0.0691 | 0.2057 | 0.2053 | 0.8702 | 0.8596 | 0.8648 | 0.233 | 39.0 | |
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| 3.0848 | 2.0 | 868 | 3.9978 | 0.2554 | 0.0651 | 0.2183 | 0.2183 | 0.8646 | 0.8589 | 0.8617 | 0.2022 | 29.1364 | |
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| 1.9491 | 3.0 | 1302 | 4.4524 | 0.2722 | 0.0714 | 0.2029 | 0.2031 | 0.8612 | 0.8618 | 0.8615 | 0.2582 | 47.0 | |
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| 1.0603 | 4.0 | 1736 | 5.4022 | 0.2465 | 0.0593 | 0.2071 | 0.2071 | 0.8464 | 0.858 | 0.8521 | 0.2294 | 42.0 | |
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| 0.5921 | 5.0 | 2170 | 6.1146 | 0.3035 | 0.072 | 0.2428 | 0.2429 | 0.8724 | 0.8571 | 0.8646 | 0.2108 | 29.0 | |
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| 0.3762 | 6.0 | 2604 | 6.8567 | 0.3035 | 0.072 | 0.2428 | 0.2429 | 0.8724 | 0.8571 | 0.8646 | 0.2108 | 29.0 | |
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### Framework versions |
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- Transformers 4.44.0 |
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- Pytorch 2.4.0 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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