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---
license: apache-2.0
base_model: google/long-t5-tglobal-large
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: LongT5-Large-NSPCC
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. -->
# LongT5-Large-NSPCC
This model is a fine-tuned version of [google/long-t5-tglobal-large](https://huggingface.co/google/long-t5-tglobal-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3710
- Rouge1: 0.4978
- Rouge2: 0.2091
- Rougel: 0.2874
- Rougelsum: 0.2871
- Gen Len: 251.3511
## 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: 0.0003
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:--------:|
| 6.0401 | 0.9960 | 188 | 2.7089 | 0.2766 | 0.0617 | 0.1655 | 0.1657 | 151.7021 |
| 2.4805 | 1.9974 | 377 | 1.8809 | 0.382 | 0.1178 | 0.2092 | 0.2092 | 211.1809 |
| 1.8093 | 2.9987 | 566 | 1.5769 | 0.4356 | 0.1527 | 0.2409 | 0.2409 | 246.1277 |
| 1.4653 | 4.0 | 755 | 1.4359 | 0.4661 | 0.1722 | 0.26 | 0.2603 | 245.0851 |
| 1.2626 | 4.9960 | 943 | 1.3908 | 0.4829 | 0.1931 | 0.2717 | 0.2717 | 239.8617 |
| 1.117 | 5.9974 | 1132 | 1.3724 | 0.4864 | 0.1988 | 0.2804 | 0.2804 | 244.4255 |
| 1.0404 | 6.9987 | 1321 | 1.3714 | 0.4914 | 0.2007 | 0.2826 | 0.2821 | 248.6915 |
| 1.0065 | 7.9682 | 1504 | 1.3710 | 0.4978 | 0.2091 | 0.2874 | 0.2871 | 251.3511 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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