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---
license: mit
base_model: facebook/bart-large-cnn
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-large-cnn-finetuned-scope-summarization-train-test-split
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-cnn-finetuned-scope-summarization-train-test-split
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co./facebook/bart-large-cnn) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1824
- Rouge1: 50.8184
- Rouge2: 30.1612
- Rougel: 36.9904
- Rougelsum: 49.4235
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| No log | 1.0 | 34 | 0.7061 | 44.9133 | 26.0507 | 34.7329 | 43.2231 |
| 0.8927 | 2.0 | 68 | 0.7190 | 46.3702 | 26.952 | 36.2244 | 44.7608 |
| 0.617 | 3.0 | 102 | 0.6815 | 50.5658 | 28.4213 | 36.4503 | 49.3129 |
| 0.617 | 4.0 | 136 | 0.7083 | 50.7172 | 28.1621 | 37.0023 | 49.3293 |
| 0.5169 | 5.0 | 170 | 0.6819 | 50.0364 | 27.5729 | 35.9607 | 48.0331 |
| 0.4614 | 6.0 | 204 | 0.7171 | 51.0974 | 29.0043 | 36.4498 | 49.6124 |
| 0.3995 | 7.0 | 238 | 0.7577 | 50.8879 | 29.3509 | 35.9144 | 49.4029 |
| 0.3995 | 8.0 | 272 | 0.8192 | 50.332 | 28.9931 | 36.458 | 48.8301 |
| 0.3185 | 9.0 | 306 | 0.8635 | 49.4501 | 27.6673 | 35.7586 | 47.891 |
| 0.2627 | 10.0 | 340 | 0.9229 | 51.0907 | 30.1515 | 36.5669 | 49.6126 |
| 0.2627 | 11.0 | 374 | 0.9910 | 49.6877 | 29.4712 | 36.5784 | 48.6087 |
| 0.1952 | 12.0 | 408 | 1.0266 | 51.3167 | 30.619 | 36.5097 | 49.7864 |
| 0.1234 | 13.0 | 442 | 1.0313 | 49.6518 | 28.2854 | 35.9773 | 48.4327 |
| 0.0859 | 14.0 | 476 | 1.0791 | 50.3139 | 29.9343 | 35.8756 | 49.0233 |
| 0.0859 | 15.0 | 510 | 1.1431 | 50.4173 | 29.047 | 36.4338 | 48.8672 |
| 0.0591 | 16.0 | 544 | 1.1455 | 51.733 | 30.786 | 37.4082 | 50.4284 |
| 0.0448 | 17.0 | 578 | 1.1353 | 49.5046 | 29.3628 | 36.2758 | 48.324 |
| 0.0341 | 18.0 | 612 | 1.1619 | 51.7577 | 31.1043 | 37.9571 | 50.6969 |
| 0.0341 | 19.0 | 646 | 1.1748 | 51.4652 | 30.6309 | 36.7434 | 49.9184 |
| 0.0255 | 20.0 | 680 | 1.1824 | 50.8184 | 30.1612 | 36.9904 | 49.4235 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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