---
base_model: google/pegasus-xsum
tags:
- generated_from_trainer
metrics:
- rouge
- precision
- recall
- f1
model-index:
- name: LLM_Teached_Pegasus_50k
  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_50k

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: 1.7193
- Rouge1: 0.4541
- Rouge2: 0.2071
- Rougel: 0.3708
- Rougelsum: 0.3708
- Gen Len: 26.4531
- Precision: 0.9082
- Recall: 0.9061
- F1: 0.907

## 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: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|:---------:|:------:|:------:|
| No log        | 1.0   | 390  | 1.8258          | 0.4338 | 0.1906 | 0.3496 | 0.3498    | 26.2967 | 0.9049    | 0.9023 | 0.9034 |
| 2.1621        | 2.0   | 781  | 1.7537          | 0.4449 | 0.2005 | 0.3633 | 0.3633    | 26.2727 | 0.9068    | 0.9044 | 0.9054 |
| 1.8794        | 3.0   | 1172 | 1.7268          | 0.4518 | 0.2061 | 0.3696 | 0.3695    | 26.4345 | 0.9078    | 0.9058 | 0.9066 |
| 1.8271        | 3.99  | 1560 | 1.7193          | 0.4541 | 0.2071 | 0.3708 | 0.3708    | 26.4531 | 0.9082    | 0.9061 | 0.907  |


### Framework versions

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