File size: 3,534 Bytes
f5dde94 003557d f5dde94 |
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 |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: led-base-16384-finetune-cnn
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. -->
# led-base-16384-finetune-cnn
This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the cnn_dailymail dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2020
- Rouge1: 24.2258
- Rouge2: 9.0151
- Rougel: 19.0336
- Rougelsum: 22.2604
- Gen Len: 20.0
## 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- 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 | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.8988 | 1.0 | 2000 | 2.0031 | 25.1709 | 10.0426 | 20.1311 | 23.1639 | 20.0 |
| 1.6038 | 2.0 | 4000 | 2.0314 | 25.0213 | 9.8701 | 19.8987 | 23.0129 | 20.0 |
| 1.3352 | 3.0 | 6000 | 2.1124 | 24.99 | 9.905 | 19.9566 | 23.0973 | 20.0 |
| 1.1173 | 4.0 | 8000 | 2.2055 | 25.0568 | 10.0949 | 19.9602 | 23.18 | 20.0 |
| 0.9566 | 5.0 | 10000 | 2.3262 | 24.941 | 9.5856 | 19.6285 | 23.042 | 20.0 |
| 0.7986 | 6.0 | 12000 | 2.4489 | 24.4114 | 9.2808 | 19.3296 | 22.5481 | 20.0 |
| 0.6685 | 7.0 | 14000 | 2.5211 | 24.467 | 9.5124 | 19.2685 | 22.5624 | 20.0 |
| 0.5601 | 8.0 | 16000 | 2.6299 | 24.6939 | 9.6533 | 19.4627 | 22.8048 | 20.0 |
| 0.4757 | 9.0 | 18000 | 2.7185 | 24.2098 | 9.1232 | 19.0181 | 22.4085 | 20.0 |
| 0.3926 | 10.0 | 20000 | 2.7947 | 24.5092 | 9.3964 | 19.2593 | 22.5592 | 20.0 |
| 0.3391 | 11.0 | 22000 | 2.8626 | 24.4731 | 9.3634 | 19.2966 | 22.5688 | 20.0 |
| 0.2872 | 12.0 | 24000 | 2.9175 | 24.5587 | 9.3888 | 19.3335 | 22.6443 | 20.0 |
| 0.2479 | 13.0 | 26000 | 2.9658 | 24.2983 | 9.1038 | 19.019 | 22.3675 | 20.0 |
| 0.213 | 14.0 | 28000 | 3.0273 | 24.4196 | 9.1481 | 19.0458 | 22.5135 | 20.0 |
| 0.1828 | 15.0 | 30000 | 3.0751 | 24.3283 | 9.2334 | 18.9771 | 22.3322 | 20.0 |
| 0.1608 | 16.0 | 32000 | 3.1185 | 24.3965 | 9.2047 | 19.0899 | 22.4666 | 20.0 |
| 0.1442 | 17.0 | 34000 | 3.1494 | 24.3832 | 9.1915 | 19.077 | 22.4366 | 20.0 |
| 0.1293 | 18.0 | 36000 | 3.1738 | 24.3796 | 9.1132 | 19.1015 | 22.3862 | 20.0 |
| 0.1165 | 19.0 | 38000 | 3.2073 | 24.2804 | 9.1018 | 19.0692 | 22.3023 | 20.0 |
| 0.1118 | 20.0 | 40000 | 3.2020 | 24.2258 | 9.0151 | 19.0336 | 22.2604 | 20.0 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|