---
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: 1.6541
- Rouge1: 0.4665
- Rouge2: 0.2182
- Rougel: 0.3824
- Rougelsum: 0.3824
- Gen Len: 26.5458
- Precision: 0.9101
- Recall: 0.9085
- F1: 0.9092

## 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: 12
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | F1     | Gen Len | Validation Loss | Precision | Recall | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:------:|:-------:|:---------------:|:---------:|:------:|:------:|:------:|:------:|:---------:|
| No log        | 1.0   | 390  | 0.9034 | 26.2967 | 1.8258          | 0.9049    | 0.9023 | 0.4338 | 0.1906 | 0.3496 | 0.3498    |
| 2.1621        | 2.0   | 781  | 0.9054 | 26.2727 | 1.7537          | 0.9068    | 0.9044 | 0.4449 | 0.2005 | 0.3633 | 0.3633    |
| 1.8794        | 3.0   | 1172 | 0.9066 | 26.4345 | 1.7268          | 0.9078    | 0.9058 | 0.4518 | 0.2061 | 0.3696 | 0.3695    |
| 1.8271        | 4.0   | 1560 | 0.9069 | 26.3971 | 1.7157          | 0.9082    | 0.906  | 0.4539 | 0.2075 | 0.3716 | 0.3714    |
| 1.8271        | 5.0   | 1951 | 0.9074 | 26.3015 | 1.7033          | 0.9087    | 0.9065 | 0.4561 | 0.2098 | 0.3735 | 0.3734    |
| 1.8067        | 6.0   | 2340 | 0.9077 | 26.4389 | 1.6897          | 0.9089    | 0.9069 | 0.4592 | 0.2114 | 0.3762 | 0.3759    |
| 1.7833        | 7.0   | 2731 | 0.9079 | 26.3745 | 1.6819          | 0.9092    | 0.9071 | 0.4598 | 0.2115 | 0.3764 | 0.376     |
| 1.7683        | 8.0   | 3120 | 1.6763 | 0.4621  | 0.2133          | 0.3791    | 0.3789 | 26.6204| 0.9094 | 0.9076 | 0.9083    |
| 1.7559        | 9.0   | 3511 | 1.6662 | 0.4632  | 0.215           | 0.38      | 0.3799 | 26.424 | 0.9098 | 0.9078 | 0.9086    |
| 1.7559        | 10.0  | 3902 | 1.6594 | 0.4651  | 0.2168          | 0.3812    | 0.3812 | 26.5425| 0.9099 | 0.9082 | 0.9089    |
| 1.7357        | 11.0  | 4293 | 1.6555 | 0.4663  | 0.2178          | 0.3824    | 0.3823 | 26.6051| 0.91   | 0.9086 | 0.9091    |
| 1.7297        | 11.99 | 4680 | 1.6541 | 0.4665  | 0.2182          | 0.3824    | 0.3824 | 26.5458| 0.9101 | 0.9085 | 0.9092    |


### Framework versions

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