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--- |
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base_model: beogradjanka/bart_multitask_finetuned_for_title_and_keyphrase_generation |
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tags: |
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- generated_from_trainer |
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metrics: |
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- rouge |
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model-index: |
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- name: finetuned_bart_for_titlekeygen_custom |
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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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# finetuned_bart_for_titlekeygen_custom |
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This model is a fine-tuned version of [beogradjanka/bart_multitask_finetuned_for_title_and_keyphrase_generation](https://huggingface.co/beogradjanka/bart_multitask_finetuned_for_title_and_keyphrase_generation) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 4.9518 |
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- Rouge1: 27.1931 |
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- Rouge2: 17.9025 |
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- Rougel: 27.0914 |
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- Rougelsum: 27.5983 |
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- Gen Len: 10.4444 |
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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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- num_epochs: 30 |
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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 | 0.91 | 5 | 4.4910 | 24.613 | 15.7516 | 23.7302 | 24.6083 | 11.0 | |
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| No log | 2.0 | 11 | 4.5644 | 23.1235 | 14.2857 | 22.4578 | 23.0522 | 9.0 | |
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| No log | 2.91 | 16 | 4.6286 | 24.7078 | 15.2652 | 24.7869 | 25.2108 | 10.0556 | |
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| No log | 4.0 | 22 | 4.6774 | 24.5206 | 17.2336 | 24.3437 | 25.1579 | 10.5 | |
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| No log | 4.91 | 27 | 4.7187 | 26.304 | 18.2158 | 26.3307 | 26.814 | 10.2778 | |
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| No log | 6.0 | 33 | 4.7962 | 26.7873 | 17.4056 | 26.8439 | 27.3369 | 10.1667 | |
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| No log | 6.91 | 38 | 4.8356 | 26.9721 | 17.3677 | 26.9926 | 27.5469 | 10.0556 | |
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| No log | 8.0 | 44 | 4.7903 | 28.0369 | 17.3677 | 28.1825 | 28.5073 | 10.0 | |
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| No log | 8.91 | 49 | 4.7801 | 27.1885 | 17.6215 | 26.6686 | 27.0771 | 10.1111 | |
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| No log | 10.0 | 55 | 4.8049 | 27.1885 | 17.6215 | 26.6686 | 27.0771 | 10.1667 | |
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| No log | 10.91 | 60 | 4.9275 | 26.8226 | 17.9025 | 26.8217 | 27.2015 | 10.0 | |
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| No log | 12.0 | 66 | 4.9744 | 26.8226 | 17.9025 | 26.8217 | 27.2015 | 10.1111 | |
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| No log | 12.91 | 71 | 4.9863 | 26.0231 | 17.9025 | 26.0376 | 26.3104 | 10.0 | |
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| No log | 14.0 | 77 | 4.9497 | 24.9154 | 17.2851 | 25.0693 | 25.3933 | 9.8333 | |
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| No log | 14.91 | 82 | 4.9272 | 24.5615 | 17.037 | 24.4986 | 25.0688 | 10.0556 | |
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| No log | 16.0 | 88 | 4.9417 | 25.307 | 17.037 | 25.3656 | 25.9459 | 10.0 | |
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| No log | 16.91 | 93 | 4.9712 | 26.7457 | 17.9025 | 26.7271 | 27.1185 | 9.9444 | |
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| No log | 18.0 | 99 | 4.9649 | 25.9581 | 17.9025 | 25.9478 | 26.2429 | 10.0556 | |
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| No log | 18.91 | 104 | 4.9305 | 27.0746 | 17.9025 | 26.9536 | 27.4569 | 10.6111 | |
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| No log | 20.0 | 110 | 4.9212 | 27.9706 | 18.5997 | 27.2022 | 27.7873 | 10.8889 | |
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| No log | 20.91 | 115 | 4.9196 | 27.7549 | 18.8225 | 27.5716 | 28.1145 | 10.6667 | |
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| No log | 22.0 | 121 | 4.9316 | 27.1931 | 17.9025 | 27.0914 | 27.5983 | 10.4444 | |
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| No log | 22.91 | 126 | 4.9525 | 27.1931 | 17.9025 | 27.0914 | 27.5983 | 10.2222 | |
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| No log | 24.0 | 132 | 4.9590 | 27.1931 | 17.9025 | 27.0914 | 27.5983 | 10.2222 | |
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| No log | 24.91 | 137 | 4.9545 | 27.1931 | 17.9025 | 27.0914 | 27.5983 | 10.2222 | |
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| No log | 26.0 | 143 | 4.9514 | 27.1931 | 17.9025 | 27.0914 | 27.5983 | 10.4444 | |
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| No log | 26.91 | 148 | 4.9515 | 27.1931 | 17.9025 | 27.0914 | 27.5983 | 10.4444 | |
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| No log | 27.27 | 150 | 4.9518 | 27.1931 | 17.9025 | 27.0914 | 27.5983 | 10.4444 | |
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### Framework versions |
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- Transformers 4.38.2 |
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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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