---
license: mit
base_model: facebook/bart-large-cnn
tags:
- generated_from_trainer
metrics:
- rouge
- bleu
model-index:
- name: LifeScienceBARTMainSections
  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. -->

# LifeScienceBARTMainSections

This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co./facebook/bart-large-cnn) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.7019
- Rouge1: 49.0793
- Rouge2: 14.8566
- Rougel: 33.334
- Rougelsum: 45.7662
- Bertscore Precision: 81.188
- Bertscore Recall: 82.9404
- Bertscore F1: 82.0519
- Bleu: 0.1030
- Gen Len: 229.2407

## 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: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Bertscore Precision | Bertscore Recall | Bertscore F1 | Bleu   | Gen Len  |
|:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------------------:|:----------------:|:------------:|:------:|:--------:|
| 6.4111        | 0.0888 | 100  | 6.3840          | 40.091  | 10.5597 | 26.7276 | 37.4414   | 78.1353             | 80.7026          | 79.3933      | 0.0735 | 229.2407 |
| 6.0433        | 0.1776 | 200  | 5.8904          | 41.0419 | 10.8596 | 27.756  | 38.5185   | 78.0408             | 80.8161          | 79.3991      | 0.0767 | 229.2407 |
| 5.6541        | 0.2664 | 300  | 5.5687          | 41.4629 | 11.3685 | 28.1111 | 38.5646   | 77.836              | 81.223           | 79.4878      | 0.0802 | 229.2407 |
| 5.4974        | 0.3552 | 400  | 5.3592          | 46.3384 | 12.5596 | 30.1004 | 43.0989   | 79.7577             | 81.8421          | 80.7827      | 0.0866 | 229.2407 |
| 5.3027        | 0.4440 | 500  | 5.1945          | 45.5757 | 12.693  | 30.676  | 42.4402   | 79.9319             | 81.977           | 80.9379      | 0.0883 | 229.2407 |
| 5.1618        | 0.5328 | 600  | 5.0456          | 46.1671 | 13.2513 | 31.2648 | 43.2104   | 80.1208             | 82.2358          | 81.161       | 0.0917 | 229.2407 |
| 5.0999        | 0.6216 | 700  | 4.9409          | 47.7896 | 14.2812 | 32.3827 | 44.2521   | 80.5408             | 82.6162          | 81.5619      | 0.0995 | 229.2407 |
| 4.971         | 0.7104 | 800  | 4.8510          | 47.59   | 14.1292 | 32.5959 | 44.307    | 80.6111             | 82.6499          | 81.6143      | 0.0988 | 229.2407 |
| 4.8843        | 0.7992 | 900  | 4.7847          | 49.0909 | 14.5478 | 33.0067 | 45.5964   | 81.0221             | 82.8266          | 81.9112      | 0.1013 | 229.2407 |
| 4.8264        | 0.8880 | 1000 | 4.7379          | 48.6746 | 14.6309 | 33.1973 | 45.4536   | 81.0718             | 82.8574          | 81.9519      | 0.1012 | 229.2407 |
| 4.8295        | 0.9767 | 1100 | 4.7019          | 49.0793 | 14.8566 | 33.334  | 45.7662   | 81.188              | 82.9404          | 82.0519      | 0.1030 | 229.2407 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1