---
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: 1.6256
- Rouge1: 0.4761
- Rouge2: 0.2113
- Rougel: 0.3732
- Rougelsum: 0.3731
- Gen Len: 41.0064

## 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: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 1.6549        | 1.0   | 1250 | 1.6621          | 0.4696 | 0.2069 | 0.3636 | 0.3636    | 42.6855 |
| 1.2925        | 2.0   | 2500 | 1.6256          | 0.4761 | 0.2113 | 0.3732 | 0.3731    | 41.0064 |


### Framework versions

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