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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-3e-05-bs-8-maxep-10 |
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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-3e-05-bs-8-maxep-10 |
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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: 3.7689 |
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- Rouge/rouge1: 0.4647 |
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- Rouge/rouge2: 0.2065 |
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- Rouge/rougel: 0.3953 |
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- Rouge/rougelsum: 0.3967 |
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- Bertscore/bertscore-precision: 0.8961 |
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- Bertscore/bertscore-recall: 0.8941 |
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- Bertscore/bertscore-f1: 0.895 |
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- Meteor: 0.4195 |
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- Gen Len: 37.9545 |
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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: 3e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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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: 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 | 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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| 0.332 | 1.0 | 109 | 2.9323 | 0.4383 | 0.1841 | 0.3691 | 0.3701 | 0.8915 | 0.8887 | 0.8899 | 0.3951 | 37.2182 | |
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| 0.3384 | 2.0 | 218 | 3.0419 | 0.4611 | 0.2038 | 0.3901 | 0.3918 | 0.8941 | 0.8913 | 0.8925 | 0.4163 | 37.1909 | |
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| 0.2354 | 3.0 | 327 | 3.2793 | 0.445 | 0.1903 | 0.3776 | 0.3785 | 0.8938 | 0.8895 | 0.8915 | 0.394 | 36.4545 | |
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| 0.1736 | 4.0 | 436 | 3.4093 | 0.4545 | 0.2 | 0.3877 | 0.3885 | 0.8939 | 0.8921 | 0.8928 | 0.4094 | 38.3818 | |
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| 0.1406 | 5.0 | 545 | 3.5183 | 0.4634 | 0.2065 | 0.394 | 0.3945 | 0.898 | 0.8925 | 0.8951 | 0.4032 | 35.5182 | |
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| 0.1108 | 6.0 | 654 | 3.6131 | 0.4667 | 0.2075 | 0.3961 | 0.3964 | 0.8966 | 0.8936 | 0.8949 | 0.4155 | 37.4364 | |
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| 0.0906 | 7.0 | 763 | 3.6935 | 0.4602 | 0.2002 | 0.3892 | 0.3903 | 0.8922 | 0.8944 | 0.8931 | 0.4116 | 39.6 | |
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| 0.0788 | 8.0 | 872 | 3.7280 | 0.4704 | 0.212 | 0.4 | 0.4018 | 0.8941 | 0.8955 | 0.8946 | 0.431 | 39.8727 | |
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| 0.0708 | 9.0 | 981 | 3.7601 | 0.468 | 0.2062 | 0.3979 | 0.3994 | 0.8953 | 0.8948 | 0.8949 | 0.4244 | 38.9727 | |
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| 0.0637 | 10.0 | 1090 | 3.7689 | 0.4647 | 0.2065 | 0.3953 | 0.3967 | 0.8961 | 0.8941 | 0.895 | 0.4195 | 37.9545 | |
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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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