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---
base_model: google/pegasus-xsum
tags:
- generated_from_trainer
metrics:
- rouge
- precision
- recall
- f1
model-index:
- name: LLM_Teached_Pegasus_50k
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. -->
# LLM_Teached_Pegasus_50k
This model is a fine-tuned version of [google/pegasus-xsum](https://huggingface.co/google/pegasus-xsum) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6258
- Rouge1: 0.4708
- Rouge2: 0.2214
- Rougel: 0.3861
- Rougelsum: 0.3863
- Gen Len: 26.5411
- Precision: 0.9108
- Recall: 0.9093
- F1: 0.9099
## 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: 20
- 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 | 0.9083 | 26.6204 | 1.6763 | 0.9094 | 0.9076 | 0.4621 | 0.2133 | 0.3791 | 0.3789 |
| 1.7559 | 9.0 | 3511 | 0.9086 | 26.424 | 1.6662 | 0.9098 | 0.9078 | 0.4632 | 0.215 | 0.38 | 0.3799 |
| 1.7559 | 10.0 | 3902 | 0.9089 | 26.5425 | 1.6594 | 0.9099 | 0.9082 | 0.4651 | 0.2168 | 0.3812 | 0.3812 |
| 1.7357 | 11.0 | 4293 | 0.9091 | 26.6051 | 1.6555 | 0.91 | 0.9086 | 0.4663 | 0.2178 | 0.3824 | 0.3823 |
| 1.7297 | 12.0 | 4680 | 0.9092 | 26.4393 | 1.6508 | 0.9103 | 0.9084 | 0.4668 | 0.2175 | 0.3823 | 0.3822 |
| 1.7165 | 13.0 | 5071 | 0.9094 | 26.6385 | 1.6451 | 0.9103 | 0.9089 | 0.4687 | 0.2191 | 0.3834 | 0.3834 |
| 1.7165 | 14.0 | 5462 | 0.9095 | 26.4156 | 1.6405 | 0.9106 | 0.9087 | 0.4691 | 0.2193 | 0.3845 | 0.3844 |
| 1.7068 | 15.0 | 5853 | 0.9097 | 26.4571 | 1.6383 | 0.9108 | 0.9089 | 0.4699 | 0.2204 | 0.3853 | 0.3853 |
| 1.7004 | 16.0 | 6240 | 1.6346 | 0.4703 | 0.2204 | 0.385 | 0.385 | 26.4247| 0.9108 | 0.9089 | 0.9097 |
| 1.6923 | 17.0 | 6631 | 1.6305 | 0.4706 | 0.221 | 0.3855 | 0.3856 | 26.4436| 0.911 | 0.9091 | 0.9099 |
| 1.6839 | 18.0 | 7022 | 1.6285 | 0.4712 | 0.2215 | 0.3862 | 0.3864 | 26.612 | 0.9106 | 0.9094 | 0.9098 |
| 1.6839 | 19.0 | 7413 | 1.6263 | 0.4709 | 0.2217 | 0.3862 | 0.3864 | 26.5291| 0.9108 | 0.9093 | 0.9099 |
| 1.6743 | 19.99 | 7800 | 1.6258 | 0.4708 | 0.2214 | 0.3861 | 0.3863 | 26.5411| 0.9108 | 0.9093 | 0.9099 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.15.0
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