---
license: apache-2.0
base_model: facebook/bart-large
tags:
- generated_from_trainer
metrics:
- rouge
- precision
- recall
- f1
model-index:
- name: LLM_Teached_Bart_100k
  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_100k

This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4623
- Rouge1: 0.4678
- Rouge2: 0.2472
- Rougel: 0.4081
- Rougelsum: 0.4082
- Gen Len: 19.8816
- Precision: 0.9185
- Recall: 0.8957
- F1: 0.9068

## 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: 24
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 96
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | Precision | Recall | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|:---------:|:------:|:------:|
| 1.6328        | 1.0   | 1041  | 1.4801          | 0.448  | 0.2243 | 0.385  | 0.385     | 19.8782 | 0.9134    | 0.893  | 0.9029 |
| 1.4598        | 2.0   | 2083  | 1.4051          | 0.4428 | 0.2273 | 0.3851 | 0.385     | 19.9344 | 0.9147    | 0.8903 | 0.9022 |
| 1.3402        | 3.0   | 3125  | 1.3840          | 0.4498 | 0.2318 | 0.3921 | 0.392     | 19.95   | 0.9158    | 0.8918 | 0.9034 |
| 1.2446        | 4.0   | 4167  | 1.3682          | 0.4604 | 0.2405 | 0.4014 | 0.4014    | 19.884  | 0.9169    | 0.8944 | 0.9054 |
| 1.1651        | 5.0   | 5208  | 1.3695          | 0.4594 | 0.2401 | 0.3995 | 0.3995    | 19.894  | 0.9173    | 0.8942 | 0.9055 |
| 1.1002        | 6.0   | 6250  | 1.3783          | 0.4607 | 0.2423 | 0.4014 | 0.4014    | 19.9118 | 0.9166    | 0.8945 | 0.9053 |
| 1.0427        | 7.0   | 7292  | 1.3851          | 0.462  | 0.2432 | 0.4028 | 0.4028    | 19.9075 | 0.9172    | 0.8946 | 0.9056 |
| 0.9881        | 8.0   | 8334  | 1.3911          | 0.4635 | 0.2442 | 0.4038 | 0.4037    | 19.9071 | 0.9177    | 0.8947 | 0.9059 |
| 0.9435        | 9.0   | 9375  | 1.4075          | 0.468  | 0.2471 | 0.4085 | 0.4084    | 19.8805 | 0.918     | 0.8959 | 0.9067 |
| 0.9035        | 10.0  | 10417 | 1.4125          | 0.4675 | 0.248  | 0.4085 | 0.4086    | 19.8811 | 0.9178    | 0.8957 | 0.9064 |
| 0.8702        | 11.0  | 11459 | 1.4219          | 0.4646 | 0.2455 | 0.405  | 0.4051    | 19.8947 | 0.9181    | 0.895  | 0.9063 |
| 0.8458        | 12.0  | 12501 | 1.4339          | 0.4643 | 0.2447 | 0.4055 | 0.4055    | 19.8985 | 0.9177    | 0.8952 | 0.9061 |
| 0.8207        | 13.0  | 13542 | 1.4430          | 0.4671 | 0.2463 | 0.4068 | 0.4069    | 19.9053 | 0.9182    | 0.8952 | 0.9064 |
| 0.7987        | 14.0  | 14584 | 1.4495          | 0.4633 | 0.2455 | 0.4046 | 0.4047    | 19.918  | 0.9179    | 0.8944 | 0.9059 |
| 0.787         | 15.0  | 15626 | 1.4560          | 0.4666 | 0.2471 | 0.407  | 0.4072    | 19.8956 | 0.9182    | 0.8953 | 0.9064 |
| 0.772         | 15.99 | 16656 | 1.4623          | 0.4678 | 0.2472 | 0.4081 | 0.4082    | 19.8816 | 0.9185    | 0.8957 | 0.9068 |


### Framework versions

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