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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: bart-base-xsum |
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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-base-xsum |
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This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8051 |
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- R1: 0.5643 |
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- R2: 0.3017 |
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- Rl: 0.5427 |
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- Rlsum: 0.5427 |
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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: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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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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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | R1 | R2 | Rl | Rlsum | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:------:| |
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| 0.8983 | 1.0 | 6377 | 0.8145 | 0.5443 | 0.2724 | 0.5212 | 0.5211 | |
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| 0.8211 | 2.0 | 12754 | 0.7940 | 0.5519 | 0.2831 | 0.5295 | 0.5295 | |
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| 0.7701 | 3.0 | 19131 | 0.7839 | 0.5569 | 0.2896 | 0.5347 | 0.5348 | |
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| 0.7046 | 4.0 | 25508 | 0.7792 | 0.5615 | 0.2956 | 0.5394 | 0.5393 | |
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| 0.6837 | 5.0 | 31885 | 0.7806 | 0.5631 | 0.2993 | 0.5416 | 0.5416 | |
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| 0.6412 | 6.0 | 38262 | 0.7816 | 0.5643 | 0.301 | 0.5427 | 0.5426 | |
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| 0.6113 | 7.0 | 44639 | 0.7881 | 0.5645 | 0.3017 | 0.5428 | 0.5428 | |
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| 0.5855 | 8.0 | 51016 | 0.7921 | 0.5651 | 0.303 | 0.5433 | 0.5432 | |
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| 0.5636 | 9.0 | 57393 | 0.7972 | 0.5649 | 0.3032 | 0.5433 | 0.5433 | |
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| 0.5482 | 10.0 | 63770 | 0.7996 | 0.565 | 0.3036 | 0.5436 | 0.5435 | |
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### Framework versions |
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- Transformers 4.22.1 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.0.0 |
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- Tokenizers 0.11.6 |
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