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---
tags:
- generated_from_trainer
datasets:
- multi_news
metrics:
- rouge
model-index:
- name: long-t5-tglobal-base-google-multimedia
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: multi_news
type: multi_news
config: default
split: train[15000:20000]
args: default
metrics:
- name: Rouge1
type: rouge
value: 0.1004
---
<!-- 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. -->
# long-t5-tglobal-base-google-multimedia
This model is a fine-tuned version of [QuangHuy54/long-t5-tglobal-base-google-multimedia](https://huggingface.co./QuangHuy54/long-t5-tglobal-base-google-multimedia) on the multi_news dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9936
- Rouge1: 0.1004
- Rouge2: 0.0347
- Rougel: 0.078
- Rougelsum: 0.078
- Gen Len: 18.995
## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 2.383 | 1.0 | 3000 | 1.9936 | 0.1004 | 0.0347 | 0.078 | 0.078 | 18.995 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
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