File size: 4,676 Bytes
6061ee1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 |
---
license: apache-2.0
base_model: facebook/bart-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: xsum_1677_bart-base
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. -->
# xsum_1677_bart-base
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6469
- Rouge1: 0.3879
- Rouge2: 0.1787
- Rougel: 0.3238
- Rougelsum: 0.3238
- Gen Len: 19.6644
## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.8336 | 0.31 | 500 | 0.7274 | 0.3493 | 0.139 | 0.2847 | 0.2847 | 19.511 |
| 0.7963 | 0.63 | 1000 | 0.6994 | 0.3637 | 0.1506 | 0.2977 | 0.2976 | 19.6179 |
| 0.7543 | 0.94 | 1500 | 0.6876 | 0.365 | 0.1531 | 0.2999 | 0.2999 | 19.5356 |
| 0.7461 | 1.25 | 2000 | 0.6795 | 0.3709 | 0.1584 | 0.3052 | 0.3051 | 19.6224 |
| 0.7193 | 1.57 | 2500 | 0.6739 | 0.3684 | 0.1593 | 0.3048 | 0.3047 | 19.5721 |
| 0.7225 | 1.88 | 3000 | 0.6666 | 0.371 | 0.16 | 0.3063 | 0.3063 | 19.5672 |
| 0.6779 | 2.2 | 3500 | 0.6660 | 0.3745 | 0.1632 | 0.31 | 0.31 | 19.5619 |
| 0.673 | 2.51 | 4000 | 0.6618 | 0.3763 | 0.1653 | 0.3117 | 0.3117 | 19.6738 |
| 0.6848 | 2.82 | 4500 | 0.6578 | 0.3803 | 0.168 | 0.3145 | 0.3145 | 19.6308 |
| 0.6526 | 3.14 | 5000 | 0.6581 | 0.3803 | 0.1679 | 0.3141 | 0.3141 | 19.6503 |
| 0.6497 | 3.45 | 5500 | 0.6555 | 0.3776 | 0.1681 | 0.3132 | 0.3133 | 19.643 |
| 0.6483 | 3.76 | 6000 | 0.6520 | 0.3803 | 0.17 | 0.3153 | 0.3152 | 19.6666 |
| 0.6249 | 4.08 | 6500 | 0.6535 | 0.383 | 0.1736 | 0.3186 | 0.3185 | 19.6371 |
| 0.628 | 4.39 | 7000 | 0.6531 | 0.3825 | 0.1728 | 0.3181 | 0.318 | 19.6159 |
| 0.6288 | 4.7 | 7500 | 0.6495 | 0.3827 | 0.1727 | 0.3181 | 0.3181 | 19.6695 |
| 0.5921 | 5.02 | 8000 | 0.6509 | 0.3825 | 0.173 | 0.318 | 0.318 | 19.6447 |
| 0.6003 | 5.33 | 8500 | 0.6513 | 0.3833 | 0.1742 | 0.3198 | 0.3197 | 19.6866 |
| 0.5922 | 5.65 | 9000 | 0.6482 | 0.3837 | 0.1737 | 0.3195 | 0.3195 | 19.719 |
| 0.5878 | 5.96 | 9500 | 0.6483 | 0.3824 | 0.1737 | 0.3185 | 0.3185 | 19.6156 |
| 0.5646 | 6.27 | 10000 | 0.6503 | 0.3851 | 0.1754 | 0.3203 | 0.3204 | 19.6693 |
| 0.5753 | 6.59 | 10500 | 0.6473 | 0.3855 | 0.1761 | 0.3206 | 0.3206 | 19.6873 |
| 0.579 | 6.9 | 11000 | 0.6467 | 0.3861 | 0.1769 | 0.3223 | 0.3223 | 19.6635 |
| 0.5865 | 7.21 | 11500 | 0.6480 | 0.3862 | 0.176 | 0.3213 | 0.3212 | 19.7016 |
| 0.5746 | 7.53 | 12000 | 0.6480 | 0.3878 | 0.1785 | 0.3235 | 0.3236 | 19.6531 |
| 0.5678 | 7.84 | 12500 | 0.6460 | 0.3868 | 0.1776 | 0.3221 | 0.322 | 19.7039 |
| 0.5584 | 8.15 | 13000 | 0.6485 | 0.3875 | 0.178 | 0.3233 | 0.3233 | 19.6565 |
| 0.5484 | 8.47 | 13500 | 0.6477 | 0.3867 | 0.1777 | 0.3223 | 0.3224 | 19.6937 |
| 0.558 | 8.78 | 14000 | 0.6468 | 0.3873 | 0.1781 | 0.323 | 0.323 | 19.6823 |
| 0.5482 | 9.1 | 14500 | 0.6475 | 0.3878 | 0.1787 | 0.3231 | 0.3232 | 19.6896 |
| 0.5551 | 9.41 | 15000 | 0.6475 | 0.388 | 0.1783 | 0.3238 | 0.3237 | 19.666 |
| 0.5488 | 9.72 | 15500 | 0.6469 | 0.3879 | 0.1787 | 0.3238 | 0.3238 | 19.6644 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|