---
tags:
- generated_from_trainer
metrics:
- rouge
- precision
- recall
- f1
model-index:
- name: LLM_Teached_PEGASUS_CNNDM_2
  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_CNNDM_2

This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7016
- Rouge1: 0.4651
- Rouge2: 0.2076
- Rougel: 0.3457
- Rougelsum: 0.3459
- Gen Len: 52.1582
- Precision: 0.906
- Recall: 0.9098
- F1: 0.9077

## 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: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- 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   | 312  | 1.7705          | 0.4551 | 0.1985 | 0.335  | 0.3351    | 51.6464 | 0.9043    | 0.9073 | 0.9056 |
| 1.8539        | 2.0   | 625  | 1.7468          | 0.4578 | 0.2016 | 0.3394 | 0.3397    | 51.0627 | 0.9054    | 0.908  | 0.9065 |
| 1.8539        | 3.0   | 937  | 1.7331          | 0.4595 | 0.2019 | 0.3389 | 0.3391    | 52.9318 | 0.9039    | 0.9089 | 0.9063 |
| 1.7903        | 4.0   | 1250 | 1.7226          | 0.4606 | 0.2032 | 0.3406 | 0.3405    | 52.8055 | 0.9046    | 0.9094 | 0.9068 |
| 1.746         | 5.0   | 1562 | 1.7132          | 0.4642 | 0.2068 | 0.3453 | 0.3453    | 51.7873 | 0.9062    | 0.9096 | 0.9077 |
| 1.746         | 6.0   | 1875 | 1.7117          | 0.463  | 0.2055 | 0.3435 | 0.3436    | 53.4382 | 0.905     | 0.91   | 0.9073 |
| 1.7173        | 7.0   | 2187 | 1.7057          | 0.4644 | 0.2073 | 0.3456 | 0.3457    | 52.1718 | 0.906     | 0.9099 | 0.9078 |
| 1.7004        | 8.0   | 2500 | 1.7033          | 0.4668 | 0.2084 | 0.3464 | 0.3466    | 51.9    | 0.9063    | 0.91   | 0.908  |
| 1.7004        | 9.0   | 2812 | 1.7027          | 0.4651 | 0.2074 | 0.3457 | 0.3458    | 52.3591 | 0.906     | 0.9099 | 0.9078 |
| 1.6888        | 9.98  | 3120 | 1.7016          | 0.4651 | 0.2076 | 0.3457 | 0.3459    | 52.1582 | 0.906     | 0.9098 | 0.9077 |


### Framework versions

- Transformers 4.36.0
- Pytorch 2.0.1+cu117
- Datasets 2.7.1
- Tokenizers 0.15.2