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---
base_model: google/pegasus-large
tags:
- generated_from_trainer
metrics:
- rouge
- precision
- recall
- f1
model-index:
- name: LLM_Teached_Pegasus_100k_FS
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_100k_FS
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: 1.4469
- Rouge1: 0.4939
- Rouge2: 0.2453
- Rougel: 0.4133
- Rougelsum: 0.4134
- Gen Len: 25.9629
- Precision: 0.9133
- Recall: 0.9138
- F1: 0.9134
## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 6
- 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 |
|:-------------:|:-----:|:-----:|:------:|:-------:|:---------------:|:---------:|:------:|:------:|:------:|:------:|:---------:|
| 1.781 | 2.0 | 1388 | 0.9088 | 26.8891 | 1.5797 | 0.908 | 0.91 | 0.4708 | 0.2219 | 0.3892 | 0.389 |
| 1.6618 | 3.0 | 2083 | 0.91 | 26.7282 | 1.5411 | 0.9094 | 0.9111 | 0.4776 | 0.2303 | 0.3977 | 0.3973 |
| 1.626 | 4.0 | 2776 | 0.911 | 26.7596 | 1.5171 | 0.9102 | 0.9121 | 0.4834 | 0.2345 | 0.402 | 0.402 |
| 1.5918 | 5.0 | 3471 | 0.9112 | 26.6476 | 1.5001 | 0.9106 | 0.9122 | 0.4853 | 0.2365 | 0.4045 | 0.4045 |
| 1.5586 | 6.0 | 4164 | 0.9116 | 26.7778 | 1.4880 | 0.9108 | 0.9127 | 0.4875 | 0.2373 | 0.4063 | 0.4063 |
| 1.5375 | 7.0 | 4858 | 1.4768 | 0.4898 | 0.24 | 0.4083 | 0.4083 | 26.3991| 0.9116 | 0.9128 | 0.912 |
| 1.5146 | 8.0 | 5553 | 1.4686 | 0.4907 | 0.241 | 0.4088 | 0.4089 | 26.156 | 0.9123 | 0.9133 | 0.9126 |
| 1.5006 | 9.0 | 6247 | 1.4636 | 0.4914 | 0.2419 | 0.4097 | 0.4099 | 26.2629| 0.9122 | 0.9135 | 0.9127 |
| 1.49 | 10.0 | 6942 | 1.4580 | 0.4911 | 0.2429 | 0.4109 | 0.411 | 26.0273| 0.9125 | 0.9133 | 0.9127 |
| 1.4749 | 11.0 | 7636 | 1.4546 | 0.4932 | 0.244 | 0.4121 | 0.4123 | 26.2304| 0.9127 | 0.9138 | 0.9131 |
| 1.4661 | 12.0 | 8331 | 1.4514 | 0.4937 | 0.2448 | 0.4126 | 0.4127 | 25.8778| 0.9133 | 0.9136 | 0.9132 |
| 1.4575 | 13.0 | 9025 | 1.4499 | 0.4947 | 0.2453 | 0.4139 | 0.414 | 26.1151| 0.913 | 0.914 | 0.9133 |
| 1.4511 | 14.0 | 9720 | 1.4478 | 0.4939 | 0.2451 | 0.4133 | 0.4134 | 26.0287| 0.9131 | 0.9138 | 0.9133 |
| 1.4519 | 15.0 | 10414 | 1.4471 | 0.4938 | 0.2451 | 0.4134 | 0.4134 | 25.9078| 0.9132 | 0.9137 | 0.9133 |
| 1.4439 | 15.99 | 11104 | 1.4469 | 0.4939 | 0.2453 | 0.4133 | 0.4134 | 25.9629| 0.9133 | 0.9138 | 0.9134 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.15.0
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