---
license: mit
base_model: facebook/bart-large-xsum
tags:
- generated_from_trainer
metrics:
- rouge
- precision
- recall
- f1
model-index:
- name: LLM_Teached_Bart_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_Bart_50k

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: 1.5590
- Rouge1: 0.4909
- Rouge2: 0.2303
- Rougel: 0.3967
- Rougelsum: 0.3965
- Gen Len: 38.2287
- Precision: 0.9063
- Recall: 0.9187
- F1: 0.9123

## 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: 4
- 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   | 390  | 1.6214          | 0.4804 | 0.2218 | 0.3873 | 0.3873    | 38.3549 | 0.9049    | 0.9166 | 0.9106 |
| 1.5842        | 2.0   | 781  | 1.5548          | 0.4874 | 0.2283 | 0.3945 | 0.3945    | 37.8604 | 0.9059    | 0.9171 | 0.9113 |
| 1.3014        | 3.0   | 1172 | 1.5461          | 0.49   | 0.2294 | 0.3975 | 0.3974    | 37.7564 | 0.9064    | 0.918  | 0.912  |
| 1.18          | 3.99  | 1560 | 1.5590          | 0.4909 | 0.2303 | 0.3967 | 0.3965    | 38.2287 | 0.9063    | 0.9187 | 0.9123 |


### Framework versions

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