---
base_model: google/pegasus-xsum
tags:
- generated_from_trainer
metrics:
- rouge
- precision
- recall
- f1
model-index:
- name: LLM_Teached_Pegasus_100k
  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

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: 1.5409
- Rouge1: 0.4869
- Rouge2: 0.2373
- Rougel: 0.406
- Rougelsum: 0.4058
- Gen Len: 25.3851
- Precision: 0.9131
- Recall: 0.9117
- F1: 0.9123

## 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: 8
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|:---------:|:------:|:------:|
| 2.1501        | 1.0   | 781  | 1.7062          | 0.4566 | 0.209  | 0.3745 | 0.3744    | 25.4655 | 0.9082    | 0.9065 | 0.9072 |
| 1.7722        | 2.0   | 1562 | 1.6314          | 0.4712 | 0.2226 | 0.3906 | 0.3904    | 25.4298 | 0.9107    | 0.909  | 0.9097 |
| 1.7218        | 3.0   | 2343 | 1.5948          | 0.4776 | 0.2284 | 0.3965 | 0.3963    | 25.6569 | 0.9112    | 0.9103 | 0.9106 |
| 1.6668        | 4.0   | 3125 | 1.5708          | 0.481  | 0.2316 | 0.4002 | 0.4       | 25.3451 | 0.9122    | 0.9107 | 0.9112 |
| 1.6437        | 5.0   | 3906 | 1.5565          | 0.4844 | 0.2346 | 0.4034 | 0.4031    | 25.482  | 0.9127    | 0.9113 | 0.9118 |
| 1.6186        | 6.0   | 4687 | 1.5476          | 0.4852 | 0.236  | 0.4047 | 0.4044    | 25.4191 | 0.9129    | 0.9115 | 0.912  |
| 1.607         | 7.0   | 5468 | 1.5426          | 0.486  | 0.2367 | 0.4052 | 0.405     | 25.4949 | 0.9129    | 0.9118 | 0.9122 |
| 1.5972        | 8.0   | 6248 | 1.5409          | 0.4869 | 0.2373 | 0.406  | 0.4058    | 25.3851 | 0.9131    | 0.9117 | 0.9123 |


### Framework versions

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