|
--- |
|
license: apache-2.0 |
|
base_model: facebook/bart-large |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- rouge |
|
- wer |
|
model-index: |
|
- name: bart_billsum_abstractive_1024_1000 |
|
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_billsum_abstractive_1024_1000 |
|
|
|
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: 1.0789 |
|
- Rouge1: 0.6795 |
|
- Rouge2: 0.4076 |
|
- Rougel: 0.6139 |
|
- Rougelsum: 0.6139 |
|
- Wer: 0.4803 |
|
- Bleurt: -0.0583 |
|
|
|
## 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: 6 |
|
- eval_batch_size: 6 |
|
- 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 | Wer | Bleurt | |
|
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:------:|:-------:| |
|
| No log | 0.14 | 250 | 1.3122 | 0.6345 | 0.3515 | 0.5637 | 0.5638 | 0.5303 | -0.3533 | |
|
| 2.3005 | 0.27 | 500 | 1.2468 | 0.6452 | 0.3662 | 0.5767 | 0.5767 | 0.5174 | -0.4992 | |
|
| 2.3005 | 0.41 | 750 | 1.1909 | 0.6513 | 0.3745 | 0.5823 | 0.5823 | 0.5094 | -0.4679 | |
|
| 1.3108 | 0.55 | 1000 | 1.1685 | 0.6605 | 0.3827 | 0.5928 | 0.5928 | 0.5037 | -0.1431 | |
|
| 1.3108 | 0.68 | 1250 | 1.1505 | 0.6671 | 0.3894 | 0.5984 | 0.5984 | 0.4996 | -0.0701 | |
|
| 1.2615 | 0.82 | 1500 | 1.1334 | 0.6616 | 0.3883 | 0.5949 | 0.5949 | 0.4953 | -0.3277 | |
|
| 1.2615 | 0.96 | 1750 | 1.1226 | 0.6692 | 0.3948 | 0.6035 | 0.6035 | 0.492 | -0.0701 | |
|
| 1.1939 | 1.09 | 2000 | 1.1148 | 0.6669 | 0.3942 | 0.6007 | 0.6007 | 0.4892 | -0.2128 | |
|
| 1.1939 | 1.23 | 2250 | 1.1110 | 0.6741 | 0.4003 | 0.6072 | 0.6072 | 0.4884 | -0.3492 | |
|
| 1.1268 | 1.36 | 2500 | 1.1111 | 0.6746 | 0.4018 | 0.6093 | 0.6094 | 0.4865 | -0.0701 | |
|
| 1.1268 | 1.5 | 2750 | 1.0927 | 0.6717 | 0.4001 | 0.6054 | 0.6054 | 0.4837 | -0.467 | |
|
| 1.0977 | 1.64 | 3000 | 1.0840 | 0.6756 | 0.4048 | 0.6099 | 0.61 | 0.4814 | -0.2661 | |
|
| 1.0977 | 1.77 | 3250 | 1.0834 | 0.673 | 0.4034 | 0.6077 | 0.6077 | 0.4808 | -0.2082 | |
|
| 1.079 | 1.91 | 3500 | 1.0789 | 0.6795 | 0.4076 | 0.6139 | 0.6139 | 0.4803 | -0.0583 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.38.2 |
|
- Pytorch 2.2.1+cu121 |
|
- Datasets 2.18.0 |
|
- Tokenizers 0.15.2 |
|
|
