---
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