metadata
license: mit
base_model: facebook/bart-large-cnn
tags:
- generated_from_trainer
model-index:
- name: bart_samsum_v2
results: []
bart_samsum_v2
This model is a fine-tuned version of facebook/bart-large-cnn on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0236
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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 8
- num_epochs: 15
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 9.4233 | 0.17 | 1 | 9.1990 |
| 9.5213 | 0.34 | 2 | 8.5394 |
| 8.7467 | 0.52 | 3 | 8.1115 |
| 8.4697 | 0.69 | 4 | 7.5747 |
| 7.752 | 0.86 | 5 | 6.8712 |
| 7.0515 | 1.03 | 6 | 5.8670 |
| 6.0874 | 1.2 | 7 | 4.6814 |
| 5.0408 | 1.38 | 8 | 3.8055 |
| 4.14 | 1.55 | 9 | 2.6678 |
| 2.9893 | 1.72 | 10 | 1.9701 |
| 2.4337 | 1.89 | 11 | 1.5191 |
| 1.9451 | 2.06 | 12 | 1.2105 |
| 1.53 | 2.24 | 13 | 0.9714 |
| 1.2369 | 2.41 | 14 | 0.7905 |
| 1.0014 | 2.58 | 15 | 0.6478 |
| 0.8419 | 2.75 | 16 | 0.5493 |
| 0.7338 | 2.92 | 17 | 0.4770 |
| 0.6393 | 3.1 | 18 | 0.4151 |
| 0.5747 | 3.27 | 19 | 0.3691 |
| 0.4962 | 3.44 | 20 | 0.3293 |
| 0.4516 | 3.61 | 21 | 0.2935 |
| 0.3995 | 3.78 | 22 | 0.2614 |
| 0.3618 | 3.96 | 23 | 0.2346 |
| 0.3246 | 4.13 | 24 | 0.2129 |
| 0.2929 | 4.3 | 25 | 0.1938 |
| 0.278 | 4.47 | 26 | 0.1770 |
| 0.2493 | 4.65 | 27 | 0.1627 |
| 0.2273 | 4.82 | 28 | 0.1500 |
| 0.2067 | 4.99 | 29 | 0.1381 |
| 0.1917 | 5.16 | 30 | 0.1274 |
| 0.1805 | 5.33 | 31 | 0.1174 |
| 0.1557 | 5.51 | 32 | 0.1081 |
| 0.1495 | 5.68 | 33 | 0.1002 |
| 0.1394 | 5.85 | 34 | 0.0933 |
| 0.1261 | 6.02 | 35 | 0.0868 |
| 0.1155 | 6.19 | 36 | 0.0809 |
| 0.1114 | 6.37 | 37 | 0.0755 |
| 0.1041 | 6.54 | 38 | 0.0705 |
| 0.0952 | 6.71 | 39 | 0.0657 |
| 0.0881 | 6.88 | 40 | 0.0615 |
| 0.0823 | 7.05 | 41 | 0.0577 |
| 0.0778 | 7.23 | 42 | 0.0545 |
| 0.071 | 7.4 | 43 | 0.0515 |
| 0.07 | 7.57 | 44 | 0.0487 |
| 0.0625 | 7.74 | 45 | 0.0463 |
| 0.0589 | 7.91 | 46 | 0.0440 |
| 0.0567 | 8.09 | 47 | 0.0422 |
| 0.0537 | 8.26 | 48 | 0.0411 |
| 0.05 | 8.43 | 49 | 0.0398 |
| 0.0472 | 8.6 | 50 | 0.0384 |
| 0.0458 | 8.77 | 51 | 0.0363 |
| 0.0455 | 8.95 | 52 | 0.0347 |
| 0.0412 | 9.12 | 53 | 0.0340 |
| 0.0414 | 9.29 | 54 | 0.0326 |
| 0.0403 | 9.46 | 55 | 0.0333 |
| 0.0384 | 9.63 | 56 | 0.0303 |
| 0.0353 | 9.81 | 57 | 0.0298 |
| 0.0348 | 9.98 | 58 | 0.0293 |
| 0.0342 | 10.15 | 59 | 0.0275 |
| 0.0311 | 10.32 | 60 | 0.0272 |
| 0.0317 | 10.49 | 61 | 0.0270 |
| 0.0315 | 10.67 | 62 | 0.0261 |
| 0.0289 | 10.84 | 63 | 0.0253 |
| 0.0285 | 11.01 | 64 | 0.0247 |
| 0.0273 | 11.18 | 65 | 0.0244 |
| 0.0277 | 11.35 | 66 | 0.0240 |
| 0.0267 | 11.53 | 67 | 0.0237 |
| 0.0263 | 11.7 | 68 | 0.0237 |
| 0.0258 | 11.87 | 69 | 0.0237 |
| 0.0254 | 12.04 | 70 | 0.0238 |
| 0.0248 | 12.22 | 71 | 0.0239 |
| 0.0246 | 12.39 | 72 | 0.0239 |
| 0.0249 | 12.56 | 73 | 0.0237 |
| 0.0239 | 12.73 | 74 | 0.0236 |
| 0.0247 | 12.9 | 75 | 0.0236 |
Framework versions
- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2