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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-lr5e-5
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-lr5e-5
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: 0.0983
- Rouge1: 0.6245
- Rouge2: 0.4439
- Rougel: 0.6142
- Sacrebleu: 35.7192
- 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: 5e-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
- lr_scheduler_warmup_steps: 500
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Sacrebleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:--------:|
| 35.3898 | 0.96 | 200 | 27.6372 | 0.0019 | 0.0 | 0.0018 | 0.0003 | 512.0 |
| 3.5712 | 1.91 | 400 | 1.9615 | 0.0171 | 0.0 | 0.0167 | 0.0225 | 39.0501 |
| 0.6489 | 2.87 | 600 | 0.5638 | 0.0052 | 0.0 | 0.0051 | 0.0256 | 39.0501 |
| 0.6017 | 3.83 | 800 | 3.2823 | 0.2419 | 0.1287 | 0.2318 | 0.6457 | 130.3556 |
| 0.3784 | 4.78 | 1000 | 0.1340 | 0.5092 | 0.3277 | 0.4978 | 26.7005 | 38.0549 |
| 0.1521 | 5.74 | 1200 | 0.1092 | 0.5782 | 0.3973 | 0.5672 | 33.2443 | 38.0501 |
| 0.2096 | 6.7 | 1400 | 0.1001 | 0.6149 | 0.4342 | 0.6046 | 34.6518 | 38.0501 |
| 0.1719 | 7.66 | 1600 | 0.0983 | 0.6245 | 0.4439 | 0.6142 | 35.7192 | 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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