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--- |
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license: mit |
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base_model: facebook/bart-large-xsum |
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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: LLM_Teached_Bart |
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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 |
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This model is a fine-tuned version of [facebook/bart-large-xsum](https://huggingface.co/facebook/bart-large-xsum) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.3237 |
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- Rouge1: 0.4756 |
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- Rouge2: 0.203 |
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- Rougel: 0.3677 |
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- Rougelsum: 0.3678 |
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- Gen Len: 41.4318 |
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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: 16 |
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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: 8 |
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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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| |
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| 1.6644 | 1.0 | 1250 | 1.6972 | 0.4687 | 0.2036 | 0.3619 | 0.362 | 43.4245 | |
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| 1.3035 | 2.0 | 2500 | 1.6463 | 0.4762 | 0.2104 | 0.3746 | 0.3747 | 39.5091 | |
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| 1.0206 | 3.0 | 3750 | 1.7278 | 0.476 | 0.2117 | 0.3743 | 0.3746 | 38.9555 | |
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| 0.8224 | 4.0 | 5000 | 1.8642 | 0.477 | 0.2094 | 0.3724 | 0.3723 | 40.5182 | |
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| 0.654 | 5.0 | 6250 | 1.9480 | 0.4757 | 0.2083 | 0.3717 | 0.3716 | 39.8736 | |
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| 0.5302 | 6.0 | 7500 | 2.1332 | 0.4773 | 0.2062 | 0.37 | 0.3699 | 40.8309 | |
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| 0.4364 | 7.0 | 8750 | 2.2474 | 0.4749 | 0.2008 | 0.3648 | 0.3648 | 42.0391 | |
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| 0.3782 | 8.0 | 10000 | 2.3237 | 0.4756 | 0.203 | 0.3677 | 0.3678 | 41.4318 | |
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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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