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bart-base-finetuned-cnn_dailymail

Text2Text Generation
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bart
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  1. README.md +26 -21
  2. model.safetensors +1 -1
  3. runs/May04_02-56-20_DESKTOP-I570M0U/events.out.tfevents.1714780714.DESKTOP-I570M0U.18935.0 +2 -2
README.md CHANGED
@@ -17,16 +17,17 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.0624
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- - Rouge1: 24.4564
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- - Rouge2: 11.9696
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- - Rougel: 20.5207
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- - Rougelsum: 23.0078
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- - Bleu 1: 4.1113
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- - Bleu 2: 2.692
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- - Bleu 3: 1.9585
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- - Meteor: 12.0483
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- - Compression rate: 4.07
 
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 5.6e-05
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- - train_batch_size: 16
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- - eval_batch_size: 128
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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: 6
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu 1 | Bleu 2 | Bleu 3 | Meteor | Compression rate |
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- |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:------:|:------:|:------:|:-------:|:----------------:|
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- | 1.3117 | 1.0 | 1875 | 1.0873 | 24.4119 | 11.8902 | 20.5092 | 22.8997 | 4.1432 | 2.7081 | 1.9647 | 12.0394 | 4.0945 |
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- | 1.0667 | 2.0 | 3750 | 1.0588 | 24.364 | 11.9692 | 20.3498 | 22.8133 | 4.0425 | 2.6521 | 1.9328 | 11.9475 | 4.1164 |
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- | 0.9644 | 3.0 | 5625 | 1.0564 | 24.2853 | 11.9445 | 20.4585 | 22.8519 | 4.0533 | 2.6698 | 1.9457 | 11.9912 | 4.1173 |
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- | 0.8876 | 4.0 | 7500 | 1.0519 | 24.2696 | 11.8337 | 20.3562 | 22.8098 | 4.1164 | 2.698 | 1.9479 | 11.9819 | 4.0777 |
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- | 0.8301 | 5.0 | 9375 | 1.0556 | 24.393 | 11.9329 | 20.4502 | 22.9487 | 4.116 | 2.693 | 1.9458 | 11.9937 | 4.0738 |
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- | 0.7897 | 6.0 | 11250 | 1.0624 | 24.4564 | 11.9696 | 20.5207 | 23.0078 | 4.1113 | 2.692 | 1.9585 | 12.0483 | 4.07 |
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.0437
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+ - Rouge1: 25.3365
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+ - Rouge2: 13.3508
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+ - Rougel: 21.4401
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+ - Rougelsum: 23.9107
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+ - Bleu 1: 3.9737
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+ - Bleu 2: 2.7698
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+ - Bleu 3: 2.0856
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+ - Meteor: 12.8165
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+ - Lungime rezumat: 11.6837
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+ - Lungime original: 48.7563
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 5.6e-05
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+ - train_batch_size: 64
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+ - eval_batch_size: 64
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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: 10
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu 1 | Bleu 2 | Bleu 3 | Meteor | Lungime rezumat | Lungime original |
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+ |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:------:|:------:|:------:|:-------:|:---------------:|:----------------:|
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+ | 1.3567 | 1.0 | 896 | 1.0741 | 25.256 | 13.2616 | 21.4201 | 23.8469 | 4.0588 | 2.8245 | 2.1231 | 12.7828 | 11.7437 | 48.7563 |
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+ | 1.0881 | 2.0 | 1792 | 1.0609 | 25.1093 | 13.0973 | 21.1393 | 23.6685 | 3.943 | 2.7211 | 2.0277 | 12.6304 | 11.758 | 48.7563 |
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+ | 1.0172 | 3.0 | 2688 | 1.0445 | 25.2209 | 13.2134 | 21.3199 | 23.8191 | 4.0205 | 2.7985 | 2.0994 | 12.7482 | 11.751 | 48.7563 |
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+ | 0.9633 | 4.0 | 3584 | 1.0392 | 25.0763 | 13.145 | 21.1885 | 23.6877 | 3.9164 | 2.7134 | 2.043 | 12.6657 | 11.6963 | 48.7563 |
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+ | 0.921 | 5.0 | 4480 | 1.0369 | 25.2214 | 13.3045 | 21.4317 | 23.8493 | 3.9533 | 2.7617 | 2.0827 | 12.7434 | 11.6727 | 48.7563 |
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+ | 0.8865 | 6.0 | 5376 | 1.0377 | 25.3824 | 13.4543 | 21.4896 | 24.0024 | 3.9731 | 2.799 | 2.1298 | 12.9173 | 11.6563 | 48.7563 |
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+ | 0.8576 | 7.0 | 6272 | 1.0347 | 25.1748 | 13.3232 | 21.3419 | 23.7755 | 3.925 | 2.7544 | 2.089 | 12.7437 | 11.6417 | 48.7563 |
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+ | 0.8353 | 8.0 | 7168 | 1.0373 | 25.3485 | 13.3938 | 21.4843 | 23.9589 | 3.9384 | 2.7462 | 2.071 | 12.8098 | 11.6407 | 48.7563 |
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+ | 0.8173 | 9.0 | 8064 | 1.0448 | 25.345 | 13.3389 | 21.4394 | 23.9221 | 3.9543 | 2.7587 | 2.0827 | 12.8046 | 11.6827 | 48.7563 |
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+ | 0.8044 | 10.0 | 8960 | 1.0437 | 25.3365 | 13.3508 | 21.4401 | 23.9107 | 3.9737 | 2.7698 | 2.0856 | 12.8165 | 11.6837 | 48.7563 |
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  ### Framework versions
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