metadata
base_model: google/pegasus-xsum
tags:
- generated_from_trainer
metrics:
- rouge
- precision
- recall
- f1
model-index:
- name: LLM_Teached_Pegasus_50k
results: []
LLM_Teached_Pegasus_50k
This model is a fine-tuned version of google/pegasus-xsum on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.6541
- Rouge1: 0.4665
- Rouge2: 0.2182
- Rougel: 0.3824
- Rougelsum: 0.3824
- Gen Len: 26.5458
- Precision: 0.9101
- Recall: 0.9085
- F1: 0.9092
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: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 12
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | F1 | Gen Len | Validation Loss | Precision | Recall | Rouge1 | Rouge2 | Rougel | Rougelsum |
|---|---|---|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 390 | 0.9034 | 26.2967 | 1.8258 | 0.9049 | 0.9023 | 0.4338 | 0.1906 | 0.3496 | 0.3498 |
| 2.1621 | 2.0 | 781 | 0.9054 | 26.2727 | 1.7537 | 0.9068 | 0.9044 | 0.4449 | 0.2005 | 0.3633 | 0.3633 |
| 1.8794 | 3.0 | 1172 | 0.9066 | 26.4345 | 1.7268 | 0.9078 | 0.9058 | 0.4518 | 0.2061 | 0.3696 | 0.3695 |
| 1.8271 | 4.0 | 1560 | 0.9069 | 26.3971 | 1.7157 | 0.9082 | 0.906 | 0.4539 | 0.2075 | 0.3716 | 0.3714 |
| 1.8271 | 5.0 | 1951 | 0.9074 | 26.3015 | 1.7033 | 0.9087 | 0.9065 | 0.4561 | 0.2098 | 0.3735 | 0.3734 |
| 1.8067 | 6.0 | 2340 | 0.9077 | 26.4389 | 1.6897 | 0.9089 | 0.9069 | 0.4592 | 0.2114 | 0.3762 | 0.3759 |
| 1.7833 | 7.0 | 2731 | 0.9079 | 26.3745 | 1.6819 | 0.9092 | 0.9071 | 0.4598 | 0.2115 | 0.3764 | 0.376 |
| 1.7683 | 8.0 | 3120 | 1.6763 | 0.4621 | 0.2133 | 0.3791 | 0.3789 | 26.6204 | 0.9094 | 0.9076 | 0.9083 |
| 1.7559 | 9.0 | 3511 | 1.6662 | 0.4632 | 0.215 | 0.38 | 0.3799 | 26.424 | 0.9098 | 0.9078 | 0.9086 |
| 1.7559 | 10.0 | 3902 | 1.6594 | 0.4651 | 0.2168 | 0.3812 | 0.3812 | 26.5425 | 0.9099 | 0.9082 | 0.9089 |
| 1.7357 | 11.0 | 4293 | 1.6555 | 0.4663 | 0.2178 | 0.3824 | 0.3823 | 26.6051 | 0.91 | 0.9086 | 0.9091 |
| 1.7297 | 11.99 | 4680 | 1.6541 | 0.4665 | 0.2182 | 0.3824 | 0.3824 | 26.5458 | 0.9101 | 0.9085 | 0.9092 |
Framework versions
- Transformers 4.36.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.15.0