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---
base_model: google/pegasus-large
tags:
- generated_from_trainer
metrics:
- rouge
- precision
- recall
- f1
model-index:
- name: LLM_Teached_Pegasus_From_Scratch
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_From_Scratch
This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5146
- Rouge1: 0.4863
- Rouge2: 0.2348
- Rougel: 0.4011
- Rougelsum: 0.4012
- Gen Len: 27.5716
- Precision: 0.9118
- Recall: 0.9131
- F1: 0.9122
## 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: 16
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | F1 | Gen Len | Validation Loss | Precision | Recall | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:------:|:-------:|:---------------:|:---------:|:------:|:------:|:------:|:------:|:---------:|
| 2.0443 | 1.0 | 521 | 0.9049 | 28.3633 | 1.7046 | 0.9041 | 0.9061 | 0.4488 | 0.203 | 0.3633 | 0.3633 |
| 1.7826 | 2.0 | 1042 | 0.9072 | 28.1949 | 1.6347 | 0.9062 | 0.9085 | 0.4616 | 0.2133 | 0.3761 | 0.3758 |
| 1.7134 | 3.0 | 1563 | 0.9084 | 28.5218 | 1.5991 | 0.9072 | 0.91 | 0.4683 | 0.2186 | 0.3824 | 0.3822 |
| 1.6664 | 4.0 | 2084 | 0.9096 | 28.2498 | 1.5767 | 0.9087 | 0.9109 | 0.4738 | 0.2233 | 0.3878 | 0.3876 |
| 1.6296 | 5.0 | 2605 | 0.9103 | 28.2396 | 1.5595 | 0.9093 | 0.9117 | 0.4775 | 0.2265 | 0.3911 | 0.391 |
| 1.5984 | 6.0 | 3126 | 0.9109 | 28.28 | 1.5468 | 0.9098 | 0.9124 | 0.4805 | 0.2284 | 0.3941 | 0.3938 |
| 1.5738 | 7.0 | 3647 | 1.5370 | 0.4807 | 0.2296 | 0.3945 | 0.3946 | 27.8378| 0.9105 | 0.9124 | 0.9113 |
| 1.5476 | 8.0 | 4168 | 1.5308 | 0.4823 | 0.2315 | 0.3963 | 0.3965 | 27.7364| 0.9108 | 0.9125 | 0.9114 |
| 1.535 | 9.0 | 4689 | 1.5261 | 0.4829 | 0.2309 | 0.3974 | 0.3974 | 27.6535| 0.911 | 0.9125 | 0.9116 |
| 1.52 | 10.0 | 5210 | 1.5231 | 0.4847 | 0.2332 | 0.3992 | 0.3993 | 27.816 | 0.911 | 0.9128 | 0.9117 |
| 1.5145 | 11.0 | 5731 | 1.5200 | 0.4851 | 0.2339 | 0.4004 | 0.4006 | 27.3604| 0.9119 | 0.9127 | 0.9121 |
| 1.5028 | 12.0 | 6252 | 1.5178 | 0.4858 | 0.2345 | 0.4001 | 0.4002 | 27.4625| 0.9118 | 0.9129 | 0.9122 |
| 1.4946 | 13.0 | 6773 | 1.5164 | 0.4859 | 0.2341 | 0.4004 | 0.4005 | 27.6789| 0.9115 | 0.9131 | 0.9121 |
| 1.4877 | 14.0 | 7294 | 1.5151 | 0.4868 | 0.235 | 0.4013 | 0.4013 | 27.5804| 0.9119 | 0.9131 | 0.9123 |
| 1.4855 | 15.0 | 7815 | 1.5146 | 0.4863 | 0.2349 | 0.4014 | 0.4016 | 27.5844| 0.9117 | 0.9131 | 0.9122 |
| 1.4782 | 16.0 | 8336 | 1.5146 | 0.4863 | 0.2348 | 0.4011 | 0.4012 | 27.5716| 0.9118 | 0.9131 | 0.9122 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.15.0
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