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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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- wer |
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model-index: |
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- name: bart_extractive_1024_750 |
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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_extractive_1024_750 |
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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: 0.9368 |
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- Rouge1: 0.7111 |
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- Rouge2: 0.4588 |
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- Rougel: 0.6541 |
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- Rougelsum: 0.6542 |
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- Wer: 0.433 |
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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: 6 |
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- eval_batch_size: 6 |
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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: 1 |
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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 | Wer | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:------:| |
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| No log | 0.13 | 250 | 1.1442 | 0.6749 | 0.4062 | 0.6133 | 0.6132 | 0.4806 | |
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| 2.053 | 0.27 | 500 | 1.0353 | 0.6859 | 0.4274 | 0.6269 | 0.6269 | 0.4586 | |
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| 2.053 | 0.4 | 750 | 1.0013 | 0.6935 | 0.4384 | 0.6351 | 0.6352 | 0.4499 | |
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| 1.1091 | 0.53 | 1000 | 0.9866 | 0.7003 | 0.4467 | 0.6416 | 0.6417 | 0.4425 | |
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| 1.1091 | 0.66 | 1250 | 0.9591 | 0.7052 | 0.4512 | 0.6469 | 0.647 | 0.4386 | |
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| 1.0491 | 0.8 | 1500 | 0.9502 | 0.7035 | 0.4517 | 0.6469 | 0.647 | 0.4366 | |
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| 1.0491 | 0.93 | 1750 | 0.9368 | 0.7111 | 0.4588 | 0.6541 | 0.6542 | 0.433 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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