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---
library_name: transformers
license: mit
base_model: facebook/bart-large-cnn
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: BART-Large-psychological-dataset
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. -->
# BART-Large-psychological-dataset
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co./facebook/bart-large-cnn) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1549
- Rouge1: 0.6621
- Rouge2: 0.4488
- Rougel: 0.5658
- Rougelsum: 0.5656
- Gen Len: 80.6204
## 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: 8
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 274 | 0.8651 | 0.6252 | 0.3953 | 0.5206 | 0.5206 | 86.9982 |
| 0.7484 | 2.0 | 548 | 0.8332 | 0.648 | 0.4301 | 0.554 | 0.5541 | 79.885 |
| 0.7484 | 3.0 | 822 | 0.8943 | 0.6498 | 0.4335 | 0.5514 | 0.5518 | 82.635 |
| 0.3207 | 4.0 | 1096 | 0.9653 | 0.6571 | 0.4422 | 0.5607 | 0.5609 | 79.9708 |
| 0.3207 | 5.0 | 1370 | 1.0514 | 0.6582 | 0.4445 | 0.5637 | 0.5639 | 79.8047 |
| 0.1557 | 6.0 | 1644 | 1.0752 | 0.6607 | 0.4476 | 0.5659 | 0.5657 | 79.6058 |
| 0.1557 | 7.0 | 1918 | 1.1302 | 0.6588 | 0.4443 | 0.5626 | 0.5626 | 80.5821 |
| 0.0845 | 8.0 | 2192 | 1.1549 | 0.6621 | 0.4488 | 0.5658 | 0.5656 | 80.6204 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1
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