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---
license: apache-2.0
base_model: google/long-t5-tglobal-xl
tags:
- generated_from_trainer
datasets:
- learn3r/summ_screen_fd_bp
metrics:
- rouge
model-index:
- name: longt5_xl_sfd_bp_15
results:
- task:
name: Summarization
type: summarization
dataset:
name: learn3r/summ_screen_fd_bp
type: learn3r/summ_screen_fd_bp
metrics:
- name: Rouge1
type: rouge
value: 29.7482
---
<!-- 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_xl_sfd_bp_15
This model is a fine-tuned version of [google/long-t5-tglobal-xl](https://huggingface.co/google/long-t5-tglobal-xl) on the learn3r/summ_screen_fd_bp dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5840
- Rouge1: 29.7482
- Rouge2: 12.0072
- Rougel: 21.348
- Rougelsum: 28.5849
- Gen Len: 503.5769
## 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: 0.001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 15.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| 2.5763 | 0.97 | 14 | 2.5415 | 10.6052 | 1.4494 | 10.4593 | 10.4801 | 509.6479 |
| 1.8998 | 1.95 | 28 | 1.7398 | 16.7989 | 4.1457 | 16.4049 | 15.1803 | 511.0 |
| 1.6403 | 2.99 | 43 | 1.5457 | 18.4716 | 5.4633 | 17.1393 | 16.9242 | 511.0 |
| 1.5012 | 3.97 | 57 | 1.5736 | 18.2259 | 5.3524 | 17.0162 | 16.7948 | 511.0 |
| 1.248 | 4.94 | 71 | 1.5482 | 20.8275 | 6.7412 | 18.0859 | 19.3113 | 511.0 |
| 1.0176 | 5.98 | 86 | 1.6254 | 21.1937 | 6.8813 | 18.411 | 19.8577 | 510.6775 |
| 0.8472 | 6.96 | 100 | 1.6212 | 26.1873 | 9.1581 | 20.393 | 24.1393 | 479.9704 |
| 0.7242 | 8.0 | 115 | 1.7231 | 23.5881 | 7.8961 | 18.7014 | 22.2999 | 506.9112 |
| 0.5876 | 8.97 | 129 | 1.9401 | 32.1851 | 12.6426 | 22.8358 | 30.6718 | 451.6982 |
| 0.4756 | 9.95 | 143 | 1.9001 | 31.353 | 12.994 | 23.1542 | 29.8375 | 455.5947 |
| 0.4042 | 10.99 | 158 | 2.1295 | 28.6425 | 11.8399 | 21.3847 | 27.0508 | 497.5355 |
| 0.3292 | 11.97 | 172 | 2.2441 | 31.8393 | 13.1308 | 22.135 | 30.5866 | 478.8107 |
| 0.2812 | 12.94 | 186 | 2.3464 | 34.4102 | 14.3607 | 23.8634 | 32.9732 | 429.9911 |
| 0.2443 | 13.98 | 201 | 2.2003 | 34.8239 | 14.8042 | 25.2438 | 33.0469 | 392.5385 |
| 0.1958 | 14.61 | 210 | 2.5840 | 29.7482 | 12.0072 | 21.348 | 28.5849 | 503.5769 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.2.1+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
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