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---
license: apache-2.0
base_model: sshleifer/distilbart-xsum-12-6
tags:
- generated_from_trainer
model-index:
- name: bart-abs-1509-0313-lr-3e-05-bs-2-maxep-6
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-abs-1509-0313-lr-3e-05-bs-2-maxep-6
This model is a fine-tuned version of [sshleifer/distilbart-xsum-12-6](https://huggingface.co/sshleifer/distilbart-xsum-12-6) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3486
- Rouge/rouge1: 0.4705
- Rouge/rouge2: 0.2108
- Rouge/rougel: 0.3877
- Rouge/rougelsum: 0.3894
- Bertscore/bertscore-precision: 0.8936
- Bertscore/bertscore-recall: 0.8936
- Bertscore/bertscore-f1: 0.8934
- Meteor: 0.4277
- Gen Len: 38.4091
## 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: 3e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge/rouge1 | Rouge/rouge2 | Rouge/rougel | Rouge/rougelsum | Bertscore/bertscore-precision | Bertscore/bertscore-recall | Bertscore/bertscore-f1 | Meteor | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:------:|:-------:|
| 1.1204 | 1.0 | 434 | 2.2199 | 0.4542 | 0.2173 | 0.3843 | 0.3855 | 0.8945 | 0.8893 | 0.8917 | 0.4072 | 37.2273 |
| 0.8222 | 2.0 | 868 | 2.3549 | 0.4613 | 0.2095 | 0.3935 | 0.3957 | 0.8994 | 0.8929 | 0.896 | 0.4089 | 36.8818 |
| 0.565 | 3.0 | 1302 | 2.6652 | 0.4686 | 0.2079 | 0.3905 | 0.3911 | 0.8943 | 0.8941 | 0.894 | 0.4207 | 39.6636 |
| 0.379 | 4.0 | 1736 | 2.9239 | 0.4614 | 0.2076 | 0.3937 | 0.3951 | 0.8962 | 0.8898 | 0.8928 | 0.401 | 34.8545 |
| 0.2543 | 5.0 | 2170 | 3.1849 | 0.4629 | 0.2086 | 0.3988 | 0.3998 | 0.8958 | 0.8914 | 0.8935 | 0.4076 | 36.1091 |
| 0.1761 | 6.0 | 2604 | 3.3486 | 0.4705 | 0.2108 | 0.3877 | 0.3894 | 0.8936 | 0.8936 | 0.8934 | 0.4277 | 38.4091 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1
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