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---
base_model: google/mt5-small
tags:
- generated_from_trainer
datasets:
- govreport-summarization
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-govreport-summarization
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: govreport-summarization
type: govreport-summarization
config: document
split: train
args: document
metrics:
- name: Rouge1
type: rouge
value: 5.4727
---
<!-- 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-small-finetuned-govreport-summarization
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co./google/mt5-small) on the govreport-summarization dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9193
- Rouge1: 5.4727
- Rouge2: 1.8064
- Rougel: 4.7904
- Rougelsum: 5.1785
## 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: 5.6e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 8.1803 | 1.0 | 225 | 3.4063 | 4.8262 | 1.0677 | 4.1029 | 4.6438 |
| 4.1012 | 2.0 | 450 | 3.2004 | 4.888 | 1.2529 | 4.0737 | 4.6698 |
| 3.8386 | 3.0 | 675 | 3.1341 | 5.0027 | 1.1715 | 4.1397 | 4.7616 |
| 3.6986 | 4.0 | 900 | 3.0698 | 5.3287 | 1.6223 | 4.6697 | 5.0159 |
| 3.6007 | 5.0 | 1125 | 3.0346 | 5.5318 | 1.7741 | 4.8195 | 5.2351 |
| 3.5376 | 6.0 | 1350 | 3.0039 | 4.5345 | 1.3055 | 4.0118 | 4.3259 |
| 3.4794 | 7.0 | 1575 | 2.9845 | 4.755 | 1.5096 | 4.2156 | 4.5376 |
| 3.4373 | 8.0 | 1800 | 2.9699 | 4.6843 | 1.409 | 4.0942 | 4.4492 |
| 3.4007 | 9.0 | 2025 | 2.9569 | 5.5517 | 1.8103 | 4.8226 | 5.2639 |
| 3.3788 | 10.0 | 2250 | 2.9415 | 5.4873 | 1.8689 | 4.8027 | 5.2162 |
| 3.3549 | 11.0 | 2475 | 2.9429 | 5.3814 | 1.7672 | 4.7337 | 5.1079 |
| 3.3386 | 12.0 | 2700 | 2.9338 | 5.4238 | 1.7718 | 4.7339 | 5.1216 |
| 3.3195 | 13.0 | 2925 | 2.9224 | 5.4666 | 1.8941 | 4.79 | 5.1824 |
| 3.311 | 14.0 | 3150 | 2.9223 | 5.4197 | 1.7975 | 4.7752 | 5.1176 |
| 3.3027 | 15.0 | 3375 | 2.9202 | 5.494 | 1.8446 | 4.7876 | 5.1981 |
| 3.2961 | 16.0 | 3600 | 2.9193 | 5.4727 | 1.8064 | 4.7904 | 5.1785 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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