---
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: 1.4433
- Rouge1: 0.4961
- Rouge2: 0.2476
- Rougel: 0.4155
- Rougelsum: 0.4154
- Gen Len: 25.8629
- Precision: 0.9136
- Recall: 0.914
- F1: 0.9137

## 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: 20
- 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  | 0.912  | 26.3991 | 1.4768          | 0.9116    | 0.9128 | 0.4898 | 0.24   | 0.4083 | 0.4083    |
| 1.5146        | 8.0   | 5553  | 0.9126 | 26.156  | 1.4686          | 0.9123    | 0.9133 | 0.4907 | 0.241  | 0.4088 | 0.4089    |
| 1.5006        | 9.0   | 6247  | 0.9127 | 26.2629 | 1.4636          | 0.9122    | 0.9135 | 0.4914 | 0.2419 | 0.4097 | 0.4099    |
| 1.49          | 10.0  | 6942  | 0.9127 | 26.0273 | 1.4580          | 0.9125    | 0.9133 | 0.4911 | 0.2429 | 0.4109 | 0.411     |
| 1.4749        | 11.0  | 7636  | 0.9131 | 26.2304 | 1.4546          | 0.9127    | 0.9138 | 0.4932 | 0.244  | 0.4121 | 0.4123    |
| 1.4661        | 12.0  | 8331  | 0.9132 | 25.8778 | 1.4514          | 0.9133    | 0.9136 | 0.4937 | 0.2448 | 0.4126 | 0.4127    |
| 1.4575        | 13.0  | 9025  | 0.9133 | 26.1151 | 1.4499          | 0.913     | 0.914  | 0.4947 | 0.2453 | 0.4139 | 0.414     |
| 1.4511        | 14.0  | 9720  | 0.9133 | 26.0287 | 1.4478          | 0.9131    | 0.9138 | 0.4939 | 0.2451 | 0.4133 | 0.4134    |
| 1.4519        | 15.0  | 10414 | 0.9133 | 25.9078 | 1.4471          | 0.9132    | 0.9137 | 0.4938 | 0.2451 | 0.4134 | 0.4134    |
| 1.4439        | 16.0  | 11104 | 1.4474 | 0.4942  | 0.2456          | 0.4133    | 0.4134 | 26.0345| 0.9131 | 0.9139 | 0.9133    |
| 1.4441        | 17.0  | 11799 | 1.4447 | 0.4945  | 0.2457          | 0.4139    | 0.414  | 25.9391| 0.9133 | 0.9138 | 0.9134    |
| 1.444         | 18.0  | 12493 | 1.4446 | 0.4957  | 0.2473          | 0.415     | 0.4151 | 26.0107| 0.9133 | 0.9141 | 0.9135    |
| 1.4375        | 19.0  | 13188 | 1.4433 | 0.4961  | 0.2473          | 0.4153    | 0.4153 | 25.8869| 0.9136 | 0.914  | 0.9136    |
| 1.4361        | 20.0  | 13880 | 1.4433 | 0.4961  | 0.2476          | 0.4155    | 0.4154 | 25.8629| 0.9136 | 0.914  | 0.9137    |


### Framework versions

- Transformers 4.36.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.15.0