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--- |
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license: apache-2.0 |
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base_model: facebook/bart-large |
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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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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: LLM_Teached_Bart_From_Scratch |
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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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# LLM_Teached_Bart_From_Scratch |
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This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.6350 |
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- Rouge1: 0.4471 |
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- Rouge2: 0.2259 |
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- Rougel: 0.3846 |
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- Rougelsum: 0.3845 |
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- Gen Len: 19.9087 |
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- Precision: 0.9156 |
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- Recall: 0.8915 |
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- F1: 0.9033 |
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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: 24 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 96 |
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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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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | F1 | Gen Len | Validation Loss | Precision | Recall | Rouge1 | Rouge2 | Rougel | Rougelsum | |
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|:-------------:|:-----:|:-----:|:------:|:-------:|:---------------:|:---------:|:------:|:------:|:------:|:------:|:---------:| |
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| 1.836 | 1.0 | 521 | 0.8971 | 19.9745 | 1.5560 | 0.9105 | 0.8843 | 0.4155 | 0.2028 | 0.3561 | 0.3559 | |
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| 1.5951 | 2.0 | 1042 | 0.8997 | 19.9353 | 1.5004 | 0.9115 | 0.8886 | 0.4333 | 0.2136 | 0.3695 | 0.3694 | |
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| 1.469 | 3.0 | 1563 | 0.9001 | 19.9385 | 1.4691 | 0.912 | 0.8888 | 0.4355 | 0.2176 | 0.3729 | 0.3728 | |
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| 1.373 | 4.0 | 2084 | 0.9003 | 19.9647 | 1.4658 | 0.9137 | 0.8877 | 0.4311 | 0.2164 | 0.3706 | 0.3704 | |
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| 1.2902 | 5.0 | 2605 | 0.9008 | 19.9498 | 1.4542 | 0.9136 | 0.8887 | 0.4368 | 0.2218 | 0.3762 | 0.376 | |
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| 1.222 | 6.0 | 3126 | 0.9018 | 19.9425 | 1.4584 | 0.914 | 0.8902 | 0.4407 | 0.223 | 0.3802 | 0.3798 | |
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| 1.1655 | 7.0 | 3647 | 0.9019 | 19.9327 | 1.4709 | 0.9145 | 0.89 | 0.4404 | 0.2246 | 0.3806 | 0.3803 | |
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| 1.11 | 8.0 | 4168 | 0.9026 | 19.9084 | 1.4724 | 0.9153 | 0.8906 | 0.4435 | 0.2269 | 0.383 | 0.3828 | |
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| 1.0629 | 9.0 | 4689 | 0.9028 | 19.928 | 1.4853 | 0.9155 | 0.8908 | 0.4431 | 0.2273 | 0.3832 | 0.383 | |
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| 1.023 | 10.0 | 5210 | 0.9021 | 19.944 | 1.5033 | 0.9152 | 0.8897 | 0.4409 | 0.2247 | 0.3819 | 0.3818 | |
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| 0.9862 | 11.0 | 5731 | 0.9034 | 19.9124 | 1.5074 | 0.9158 | 0.8916 | 0.4479 | 0.2278 | 0.3862 | 0.386 | |
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| 0.957 | 12.0 | 6252 | 0.903 | 19.9033 | 1.5184 | 0.9159 | 0.8909 | 0.4461 | 0.2264 | 0.3846 | 0.3847 | |
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| 0.9315 | 13.0 | 6773 | 0.9031 | 19.9084 | 1.5269 | 0.9156 | 0.8912 | 0.4473 | 0.2284 | 0.386 | 0.3858 | |
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| 0.9093 | 14.0 | 7294 | 0.9029 | 19.9135 | 1.5311 | 0.9155 | 0.8909 | 0.4453 | 0.2273 | 0.3846 | 0.3843 | |
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| 0.8927 | 15.0 | 7815 | 0.9029 | 19.9065 | 1.5351 | 0.9156 | 0.8909 | 0.4457 | 0.2267 | 0.3842 | 0.384 | |
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| 0.8773 | 16.0 | 8336 | 0.9025 | 19.9425 | 1.5440 | 0.9151 | 0.8905 | 0.4427 | 0.225 | 0.382 | 0.382 | |
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| 0.8806 | 17.0 | 8857 | 0.9036 | 19.8851 | 1.5510 | 0.9159 | 0.8919 | 0.4495 | 0.2279 | 0.3868 | 0.3869 | |
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| 0.8683 | 18.0 | 9378 | 0.9038 | 19.8829 | 1.5679 | 0.9161 | 0.8921 | 0.4473 | 0.2282 | 0.3856 | 0.3857 | |
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| 0.8413 | 19.0 | 9899 | 0.9035 | 19.9135 | 1.5745 | 0.9159 | 0.8918 | 0.4492 | 0.2282 | 0.3861 | 0.3864 | |
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| 0.8257 | 20.0 | 10420 | 0.9031 | 19.8996 | 1.5835 | 0.9153 | 0.8915 | 0.4471 | 0.2266 | 0.3852 | 0.3853 | |
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| 0.8097 | 21.0 | 10941 | 0.9034 | 19.9073 | 1.5957 | 0.9156 | 0.8919 | 0.4472 | 0.2271 | 0.3856 | 0.3856 | |
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| 0.7926 | 22.0 | 11462 | 0.9034 | 19.892 | 1.5956 | 0.9159 | 0.8916 | 0.4479 | 0.2282 | 0.3855 | 0.3857 | |
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| 0.7841 | 23.0 | 11983 | 0.9028 | 19.912 | 1.5990 | 0.9155 | 0.8908 | 0.4444 | 0.2261 | 0.3833 | 0.3834 | |
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| 0.7669 | 24.0 | 12504 | 1.6097 | 0.4491 | 0.2284 | 0.3872 | 0.387 | 19.9007| 0.9162 | 0.892 | 0.9037 | |
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| 0.7733 | 25.0 | 13025 | 1.6060 | 0.4442 | 0.2257 | 0.3827 | 0.3828 | 19.9178| 0.9154 | 0.8906 | 0.9027 | |
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| 0.7631 | 26.0 | 13546 | 1.6187 | 0.4472 | 0.2276 | 0.3861 | 0.3861 | 19.9175| 0.9154 | 0.8915 | 0.9031 | |
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| 0.7505 | 27.0 | 14067 | 1.6208 | 0.4463 | 0.227 | 0.3852 | 0.3851 | 19.8967| 0.9155 | 0.8914 | 0.9031 | |
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| 0.7413 | 28.0 | 14588 | 1.6237 | 0.4468 | 0.2273 | 0.3854 | 0.3853 | 19.9153| 0.9159 | 0.8912 | 0.9032 | |
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| 0.7348 | 29.0 | 15109 | 1.6312 | 0.4482 | 0.2268 | 0.3858 | 0.3858 | 19.8938| 0.9158 | 0.8918 | 0.9035 | |
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| 0.7286 | 30.0 | 15630 | 1.6350 | 0.4471 | 0.2259 | 0.3846 | 0.3845 | 19.9087| 0.9156 | 0.8915 | 0.9033 | |
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### Framework versions |
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- Transformers 4.36.0 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.14.5 |
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- Tokenizers 0.15.0 |
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