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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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datasets: |
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- mediasum |
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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: Bart_mediasum |
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results: |
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- task: |
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name: Sequence-to-sequence Language Modeling |
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type: text2text-generation |
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dataset: |
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name: mediasum |
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type: mediasum |
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config: roberta_prepended |
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split: validation |
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args: roberta_prepended |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 0.3236 |
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- name: Precision |
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type: precision |
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value: 0.8858 |
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- name: Recall |
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type: recall |
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value: 0.8739 |
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- name: F1 |
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type: f1 |
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value: 0.8795 |
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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_mediasum |
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This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the mediasum dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.9021 |
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- Rouge1: 0.3236 |
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- Rouge2: 0.1651 |
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- Rougel: 0.2953 |
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- Rougelsum: 0.2953 |
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- Gen Len: 15.7946 |
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- Precision: 0.8858 |
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- Recall: 0.8739 |
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- F1: 0.8795 |
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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: 4 |
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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 | Precision | Recall | F1 | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|:---------:|:------:|:------:| |
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| 2.1171 | 1.0 | 4621 | 2.0135 | 0.3138 | 0.1556 | 0.2853 | 0.2853 | 16.4704 | 0.8836 | 0.8717 | 0.8773 | |
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| 1.9804 | 2.0 | 9242 | 1.9440 | 0.3147 | 0.1581 | 0.2864 | 0.2866 | 16.2207 | 0.8831 | 0.8725 | 0.8775 | |
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| 1.8971 | 3.0 | 13863 | 1.9157 | 0.3209 | 0.1638 | 0.2925 | 0.2926 | 15.4676 | 0.8857 | 0.8733 | 0.8792 | |
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| 1.8449 | 4.0 | 18484 | 1.9021 | 0.3236 | 0.1651 | 0.2953 | 0.2953 | 15.7946 | 0.8858 | 0.8739 | 0.8795 | |
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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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