---
license: mit
base_model: facebook/bart-large-xsum
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: LLM_Teached_Bart
  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_Bart

This model is a fine-tuned version of [facebook/bart-large-xsum](https://huggingface.co/facebook/bart-large-xsum) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3237
- Rouge1: 0.4756
- Rouge2: 0.203
- Rougel: 0.3677
- Rougelsum: 0.3678
- Gen Len: 41.4318

## 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: 8
- seed: 42
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 1.6644        | 1.0   | 1250  | 1.6972          | 0.4687 | 0.2036 | 0.3619 | 0.362     | 43.4245 |
| 1.3035        | 2.0   | 2500  | 1.6463          | 0.4762 | 0.2104 | 0.3746 | 0.3747    | 39.5091 |
| 1.0206        | 3.0   | 3750  | 1.7278          | 0.476  | 0.2117 | 0.3743 | 0.3746    | 38.9555 |
| 0.8224        | 4.0   | 5000  | 1.8642          | 0.477  | 0.2094 | 0.3724 | 0.3723    | 40.5182 |
| 0.654         | 5.0   | 6250  | 1.9480          | 0.4757 | 0.2083 | 0.3717 | 0.3716    | 39.8736 |
| 0.5302        | 6.0   | 7500  | 2.1332          | 0.4773 | 0.2062 | 0.37   | 0.3699    | 40.8309 |
| 0.4364        | 7.0   | 8750  | 2.2474          | 0.4749 | 0.2008 | 0.3648 | 0.3648    | 42.0391 |
| 0.3782        | 8.0   | 10000 | 2.3237          | 0.4756 | 0.203  | 0.3677 | 0.3678    | 41.4318 |


### Framework versions

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