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--- |
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license: mit |
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base_model: facebook/bart-large-cnn |
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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: PTS-Bart-Large-CNN |
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results: |
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- task: |
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type: summarization |
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name: Summarization |
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dataset: |
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name: PTS Dataset |
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type: PTS-Dataset |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 0.6376 |
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- name: Rouge2 |
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type: rouge |
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value: 0.4143 |
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- name: Rougel |
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type: rouge |
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value: 0.538 |
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- name: Rougelsum |
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type: rouge |
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value: 0.5387 |
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pipeline_tag: summarization |
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datasets: |
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- ahmedmbutt/PTS-Dataset |
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language: |
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- en |
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library_name: transformers |
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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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# PTS-Bart-Large-CNN |
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This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co./facebook/bart-large-cnn) on the PTS dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.2638 |
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- Rouge1: 0.6376 |
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- Rouge2: 0.4143 |
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- Rougel: 0.538 |
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- Rougelsum: 0.5387 |
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- Gen Len: 76.8417 |
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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: 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: 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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| No log | 1.0 | 180 | 0.8748 | 0.6166 | 0.3827 | 0.5058 | 0.5055 | 77.6583 | |
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| No log | 2.0 | 360 | 0.8774 | 0.6307 | 0.4064 | 0.5302 | 0.531 | 77.5111 | |
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| 0.6761 | 3.0 | 540 | 0.9064 | 0.635 | 0.4052 | 0.5309 | 0.5311 | 76.2833 | |
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| 0.6761 | 4.0 | 720 | 1.0386 | 0.6329 | 0.4038 | 0.5261 | 0.5262 | 78.4889 | |
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| 0.6761 | 5.0 | 900 | 1.0993 | 0.6285 | 0.4016 | 0.5239 | 0.5246 | 77.0083 | |
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| 0.2016 | 6.0 | 1080 | 1.2025 | 0.6351 | 0.4126 | 0.5351 | 0.5356 | 76.0722 | |
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| 0.2016 | 7.0 | 1260 | 1.2399 | 0.6356 | 0.4108 | 0.5362 | 0.5368 | 78.5361 | |
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| 0.2016 | 8.0 | 1440 | 1.2638 | 0.6376 | 0.4143 | 0.538 | 0.5387 | 76.8417 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
