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---
license: apache-2.0
base_model: facebook/bart-large
tags:
- generated_from_trainer
metrics:
- rouge
- wer
model-index:
- name: bart_extractive_512_375
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_extractive_512_375
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: 0.9965
- Rouge1: 0.6939
- Rouge2: 0.4349
- Rougel: 0.6334
- Rougelsum: 0.6333
- Wer: 0.4534
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:------:|
| No log | 0.13 | 250 | 1.2428 | 0.6513 | 0.3723 | 0.5834 | 0.5833 | 0.5119 |
| 2.1522 | 0.27 | 500 | 1.1468 | 0.6717 | 0.3957 | 0.605 | 0.6049 | 0.494 |
| 2.1522 | 0.4 | 750 | 1.1064 | 0.6729 | 0.404 | 0.609 | 0.609 | 0.483 |
| 1.2231 | 0.53 | 1000 | 1.0908 | 0.6762 | 0.4078 | 0.6116 | 0.6115 | 0.479 |
| 1.2231 | 0.66 | 1250 | 1.0726 | 0.6774 | 0.4108 | 0.6137 | 0.6136 | 0.4755 |
| 1.1583 | 0.8 | 1500 | 1.0581 | 0.6868 | 0.4196 | 0.6246 | 0.6245 | 0.4714 |
| 1.1583 | 0.93 | 1750 | 1.0534 | 0.6833 | 0.4209 | 0.6215 | 0.6214 | 0.4686 |
| 1.1133 | 1.06 | 2000 | 1.0330 | 0.6909 | 0.4263 | 0.6297 | 0.6297 | 0.4647 |
| 1.1133 | 1.2 | 2250 | 1.0288 | 0.6929 | 0.4293 | 0.631 | 0.6309 | 0.4626 |
| 1.0198 | 1.33 | 2500 | 1.0204 | 0.6925 | 0.4303 | 0.6305 | 0.6305 | 0.4601 |
| 1.0198 | 1.46 | 2750 | 1.0097 | 0.6965 | 0.4336 | 0.6349 | 0.6348 | 0.4582 |
| 1.0204 | 1.6 | 3000 | 1.0087 | 0.6976 | 0.4359 | 0.6361 | 0.636 | 0.4565 |
| 1.0204 | 1.73 | 3250 | 1.0042 | 0.6949 | 0.4345 | 0.6342 | 0.6342 | 0.4557 |
| 0.9889 | 1.86 | 3500 | 0.9965 | 0.696 | 0.4366 | 0.6352 | 0.6351 | 0.4534 |
| 0.9889 | 1.99 | 3750 | 0.9965 | 0.6939 | 0.4349 | 0.6334 | 0.6333 | 0.4534 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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