metadata
license: apache-2.0
base_model: facebook/bart-large
tags:
- generated_from_trainer
metrics:
- rouge
- precision
- recall
- f1
model-index:
- name: LLM_Teached_Bart_From_Scratch
results: []
LLM_Teached_Bart_From_Scratch
This model is a fine-tuned version of facebook/bart-large on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.6053
- Rouge1: 0.4481
- Rouge2: 0.2283
- Rougel: 0.3861
- Rougelsum: 0.3863
- Gen Len: 19.9029
- Precision: 0.9159
- Recall: 0.8916
- F1: 0.9034
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: 24
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 96
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 24
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | F1 | Gen Len | Validation Loss | Precision | Recall | Rouge1 | Rouge2 | Rougel | Rougelsum |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1.836 | 1.0 | 521 | 0.8971 | 19.9745 | 1.5560 | 0.9105 | 0.8843 | 0.4155 | 0.2028 | 0.3561 | 0.3559 |
| 1.5951 | 2.0 | 1042 | 0.8997 | 19.9353 | 1.5004 | 0.9115 | 0.8886 | 0.4333 | 0.2136 | 0.3695 | 0.3694 |
| 1.469 | 3.0 | 1563 | 0.9001 | 19.9385 | 1.4691 | 0.912 | 0.8888 | 0.4355 | 0.2176 | 0.3729 | 0.3728 |
| 1.373 | 4.0 | 2084 | 0.9003 | 19.9647 | 1.4658 | 0.9137 | 0.8877 | 0.4311 | 0.2164 | 0.3706 | 0.3704 |
| 1.2902 | 5.0 | 2605 | 0.9008 | 19.9498 | 1.4542 | 0.9136 | 0.8887 | 0.4368 | 0.2218 | 0.3762 | 0.376 |
| 1.222 | 6.0 | 3126 | 0.9018 | 19.9425 | 1.4584 | 0.914 | 0.8902 | 0.4407 | 0.223 | 0.3802 | 0.3798 |
| 1.1655 | 7.0 | 3647 | 0.9019 | 19.9327 | 1.4709 | 0.9145 | 0.89 | 0.4404 | 0.2246 | 0.3806 | 0.3803 |
| 1.11 | 8.0 | 4168 | 0.9026 | 19.9084 | 1.4724 | 0.9153 | 0.8906 | 0.4435 | 0.2269 | 0.383 | 0.3828 |
| 1.0629 | 9.0 | 4689 | 0.9028 | 19.928 | 1.4853 | 0.9155 | 0.8908 | 0.4431 | 0.2273 | 0.3832 | 0.383 |
| 1.023 | 10.0 | 5210 | 0.9021 | 19.944 | 1.5033 | 0.9152 | 0.8897 | 0.4409 | 0.2247 | 0.3819 | 0.3818 |
| 0.9862 | 11.0 | 5731 | 0.9034 | 19.9124 | 1.5074 | 0.9158 | 0.8916 | 0.4479 | 0.2278 | 0.3862 | 0.386 |
| 0.957 | 12.0 | 6252 | 0.903 | 19.9033 | 1.5184 | 0.9159 | 0.8909 | 0.4461 | 0.2264 | 0.3846 | 0.3847 |
| 0.9315 | 13.0 | 6773 | 0.9031 | 19.9084 | 1.5269 | 0.9156 | 0.8912 | 0.4473 | 0.2284 | 0.386 | 0.3858 |
| 0.9093 | 14.0 | 7294 | 0.9029 | 19.9135 | 1.5311 | 0.9155 | 0.8909 | 0.4453 | 0.2273 | 0.3846 | 0.3843 |
| 0.8927 | 15.0 | 7815 | 0.9029 | 19.9065 | 1.5351 | 0.9156 | 0.8909 | 0.4457 | 0.2267 | 0.3842 | 0.384 |
| 0.8773 | 16.0 | 8336 | 0.9025 | 19.9425 | 1.5440 | 0.9151 | 0.8905 | 0.4427 | 0.225 | 0.382 | 0.382 |
| 0.8806 | 17.0 | 8857 | 0.9036 | 19.8851 | 1.5510 | 0.9159 | 0.8919 | 0.4495 | 0.2279 | 0.3868 | 0.3869 |
| 0.8683 | 18.0 | 9378 | 1.5679 | 0.4473 | 0.2282 | 0.3856 | 0.3857 | 19.8829 | 0.9161 | 0.8921 | 0.9038 |
| 0.8413 | 19.0 | 9899 | 1.5745 | 0.4492 | 0.2282 | 0.3861 | 0.3864 | 19.9135 | 0.9159 | 0.8918 | 0.9035 |
| 0.8257 | 20.0 | 10420 | 1.5835 | 0.4471 | 0.2266 | 0.3852 | 0.3853 | 19.8996 | 0.9153 | 0.8915 | 0.9031 |
| 0.8097 | 21.0 | 10941 | 1.5957 | 0.4472 | 0.2271 | 0.3856 | 0.3856 | 19.9073 | 0.9156 | 0.8919 | 0.9034 |
| 0.7926 | 22.0 | 11462 | 1.5956 | 0.4479 | 0.2282 | 0.3855 | 0.3857 | 19.892 | 0.9159 | 0.8916 | 0.9034 |
| 0.7841 | 23.0 | 11983 | 1.5990 | 0.4444 | 0.2261 | 0.3833 | 0.3834 | 19.912 | 0.9155 | 0.8908 | 0.9028 |
| 0.7669 | 24.0 | 12504 | 1.6053 | 0.4481 | 0.2283 | 0.3861 | 0.3863 | 19.9029 | 0.9159 | 0.8916 | 0.9034 |
Framework versions
- Transformers 4.36.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.15.0