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---
license: apache-2.0
base_model: google/mt5-base
tags:
- generated_from_trainer
metrics:
- rouge
- sacrebleu
model-index:
- name: mT5-TextSimp-LT-BatchSize8-lr1e-4
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. -->
# mT5-TextSimp-LT-BatchSize8-lr1e-4
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0826
- Rouge1: 0.6956
- Rouge2: 0.532
- Rougel: 0.6875
- Sacrebleu: 41.0349
- Gen Len: 38.0501
## 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: 0.0001
- 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Sacrebleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 22.5133 | 0.96 | 200 | 14.4822 | 0.0057 | 0.0 | 0.0056 | 0.0013 | 512.0 |
| 1.0276 | 1.91 | 400 | 0.7352 | 0.022 | 0.0005 | 0.0215 | 0.0232 | 41.4702 |
| 0.6477 | 2.87 | 600 | 1.5193 | 0.1021 | 0.012 | 0.0954 | 0.0573 | 83.3723 |
| 0.1784 | 3.83 | 800 | 0.1149 | 0.6014 | 0.4222 | 0.5898 | 32.2723 | 38.0501 |
| 0.158 | 4.78 | 1000 | 0.0930 | 0.6546 | 0.4822 | 0.6463 | 37.3842 | 38.0501 |
| 0.1059 | 5.74 | 1200 | 0.0884 | 0.6714 | 0.4983 | 0.6635 | 39.0129 | 38.0501 |
| 0.1542 | 6.7 | 1400 | 0.0830 | 0.688 | 0.5184 | 0.6803 | 40.419 | 38.0501 |
| 0.1206 | 7.66 | 1600 | 0.0826 | 0.6956 | 0.532 | 0.6875 | 41.0349 | 38.0501 |
### Framework versions
- Transformers 4.33.0
- Pytorch 2.1.2+cu121
- Datasets 2.14.4
- Tokenizers 0.13.3
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