File size: 6,464 Bytes
c164e30 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 |
---
license: mit
base_model: gpt2-large
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: gpt2-large-finetuned2
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. -->
# gpt2-large-finetuned2
This model is a fine-tuned version of [gpt2-large](https://huggingface.co/gpt2-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6494
- Rouge1: 0.9235
- Rouge2: 0.9153
- Rougel: 0.9235
- Rougelsum: 0.9235
- Gen Len: 17.061
## 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: 0.0003
- train_batch_size: 32
- eval_batch_size: 32
- seed: 1
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 2.4757 | 1.0 | 278 | 1.5505 | 0.9312 | 0.9247 | 0.9312 | 0.9312 | 17.061 |
| 1.6314 | 2.0 | 556 | 1.2537 | 0.929 | 0.9222 | 0.929 | 0.929 | 17.061 |
| 1.3746 | 3.0 | 834 | 1.1054 | 0.9246 | 0.917 | 0.9246 | 0.9247 | 17.061 |
| 1.225 | 4.0 | 1112 | 1.0012 | 0.9294 | 0.9226 | 0.9293 | 0.9293 | 17.061 |
| 1.108 | 5.0 | 1390 | 0.9411 | 0.9253 | 0.9177 | 0.9253 | 0.9253 | 17.061 |
| 1.0381 | 6.0 | 1668 | 0.8901 | 0.9247 | 0.9173 | 0.9247 | 0.9247 | 17.061 |
| 0.9722 | 7.0 | 1946 | 0.8340 | 0.9247 | 0.917 | 0.9247 | 0.9247 | 17.061 |
| 0.9134 | 8.0 | 2224 | 0.7975 | 0.9236 | 0.9156 | 0.9236 | 0.9237 | 17.061 |
| 0.8894 | 9.0 | 2502 | 0.7745 | 0.9231 | 0.9158 | 0.9231 | 0.9232 | 17.061 |
| 0.8387 | 10.0 | 2780 | 0.7567 | 0.9212 | 0.9132 | 0.9212 | 0.9212 | 17.061 |
| 0.8224 | 11.0 | 3058 | 0.7374 | 0.9232 | 0.9152 | 0.9232 | 0.9232 | 17.061 |
| 0.8071 | 12.0 | 3336 | 0.7298 | 0.9237 | 0.9158 | 0.9237 | 0.9237 | 17.061 |
| 0.7973 | 13.0 | 3614 | 0.7209 | 0.9238 | 0.9161 | 0.9238 | 0.9238 | 17.061 |
| 0.7715 | 14.0 | 3892 | 0.7217 | 0.9231 | 0.915 | 0.9231 | 0.9231 | 17.061 |
| 0.771 | 15.0 | 4170 | 0.7085 | 0.9224 | 0.9139 | 0.9224 | 0.9224 | 17.061 |
| 0.7617 | 16.0 | 4448 | 0.7041 | 0.9211 | 0.9123 | 0.9211 | 0.9211 | 17.061 |
| 0.7603 | 17.0 | 4726 | 0.7004 | 0.9226 | 0.9146 | 0.9226 | 0.9227 | 17.061 |
| 0.7539 | 18.0 | 5004 | 0.6976 | 0.9253 | 0.9173 | 0.9252 | 0.9253 | 17.061 |
| 0.741 | 19.0 | 5282 | 0.6907 | 0.9229 | 0.9146 | 0.9229 | 0.9229 | 17.061 |
| 0.7422 | 20.0 | 5560 | 0.6898 | 0.9222 | 0.9141 | 0.9222 | 0.9222 | 17.061 |
| 0.7333 | 21.0 | 5838 | 0.6880 | 0.9223 | 0.9138 | 0.9223 | 0.9223 | 17.061 |
| 0.7378 | 22.0 | 6116 | 0.6837 | 0.9222 | 0.914 | 0.9222 | 0.9222 | 17.061 |
| 0.723 | 23.0 | 6394 | 0.6849 | 0.9225 | 0.914 | 0.9225 | 0.9225 | 17.061 |
| 0.7277 | 24.0 | 6672 | 0.6791 | 0.9235 | 0.9148 | 0.9235 | 0.9235 | 17.061 |
| 0.7222 | 25.0 | 6950 | 0.6834 | 0.9267 | 0.9189 | 0.9267 | 0.9267 | 17.061 |
| 0.7235 | 26.0 | 7228 | 0.6749 | 0.9221 | 0.9139 | 0.9221 | 0.9221 | 17.061 |
| 0.7207 | 27.0 | 7506 | 0.6741 | 0.9231 | 0.9149 | 0.9231 | 0.9231 | 17.061 |
| 0.7106 | 28.0 | 7784 | 0.6718 | 0.9224 | 0.9141 | 0.9224 | 0.9224 | 17.061 |
| 0.7086 | 29.0 | 8062 | 0.6706 | 0.9233 | 0.9153 | 0.9233 | 0.9233 | 17.061 |
| 0.7086 | 30.0 | 8340 | 0.6680 | 0.9241 | 0.9161 | 0.9241 | 0.9241 | 17.061 |
| 0.7081 | 31.0 | 8618 | 0.6678 | 0.9257 | 0.9177 | 0.9257 | 0.9257 | 17.061 |
| 0.6977 | 32.0 | 8896 | 0.6651 | 0.9229 | 0.9146 | 0.9229 | 0.9229 | 17.061 |
| 0.6937 | 33.0 | 9174 | 0.6634 | 0.9247 | 0.9167 | 0.9246 | 0.9247 | 17.061 |
| 0.6998 | 34.0 | 9452 | 0.6636 | 0.9243 | 0.916 | 0.9243 | 0.9243 | 17.061 |
| 0.6932 | 35.0 | 9730 | 0.6627 | 0.9254 | 0.9175 | 0.9254 | 0.9254 | 17.061 |
| 0.6978 | 36.0 | 10008 | 0.6612 | 0.9236 | 0.9154 | 0.9236 | 0.9236 | 17.061 |
| 0.6881 | 37.0 | 10286 | 0.6612 | 0.9251 | 0.9174 | 0.9251 | 0.9251 | 17.061 |
| 0.6874 | 38.0 | 10564 | 0.6589 | 0.9247 | 0.9167 | 0.9247 | 0.9247 | 17.061 |
| 0.6898 | 39.0 | 10842 | 0.6579 | 0.9235 | 0.9153 | 0.9235 | 0.9235 | 17.061 |
| 0.6857 | 40.0 | 11120 | 0.6568 | 0.9231 | 0.915 | 0.9231 | 0.9232 | 17.061 |
| 0.6751 | 41.0 | 11398 | 0.6554 | 0.924 | 0.9161 | 0.924 | 0.924 | 17.061 |
| 0.6782 | 42.0 | 11676 | 0.6547 | 0.9243 | 0.9164 | 0.9243 | 0.9243 | 17.061 |
| 0.6775 | 43.0 | 11954 | 0.6537 | 0.9242 | 0.9162 | 0.9242 | 0.9242 | 17.061 |
| 0.6764 | 44.0 | 12232 | 0.6530 | 0.923 | 0.9148 | 0.923 | 0.923 | 17.061 |
| 0.6741 | 45.0 | 12510 | 0.6524 | 0.9242 | 0.9161 | 0.9242 | 0.9242 | 17.061 |
| 0.6638 | 46.0 | 12788 | 0.6515 | 0.9241 | 0.9159 | 0.9241 | 0.9241 | 17.061 |
| 0.6634 | 47.0 | 13066 | 0.6509 | 0.9242 | 0.916 | 0.9242 | 0.9242 | 17.061 |
| 0.6614 | 48.0 | 13344 | 0.6500 | 0.9238 | 0.9156 | 0.9238 | 0.9238 | 17.061 |
| 0.6595 | 49.0 | 13622 | 0.6495 | 0.9236 | 0.9154 | 0.9236 | 0.9236 | 17.061 |
| 0.6541 | 50.0 | 13900 | 0.6494 | 0.9235 | 0.9153 | 0.9235 | 0.9235 | 17.061 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1
- Datasets 2.14.6
- Tokenizers 0.14.1
|