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---
base_model: /exports/eddie/scratch/s1970716/models/summarization/longt5_xl_summ_screen/checkpoint-140
tags:
- generated_from_trainer
datasets:
- tau/scrolls
metrics:
- rouge
model-index:
- name: longt5_xl_summ_screen_20
results:
- task:
name: Summarization
type: summarization
dataset:
name: tau/scrolls summ_screen_fd
type: tau/scrolls
config: summ_screen_fd
split: validation
args: summ_screen_fd
metrics:
- name: Rouge1
type: rouge
value: 28.1708
---
<!-- 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_summ_screen_20
This model is a fine-tuned version of [/exports/eddie/scratch/s1970716/models/summarization/longt5_xl_summ_screen/checkpoint-140](https://huggingface.co.//exports/eddie/scratch/s1970716/models/summarization/longt5_xl_summ_screen/checkpoint-140) on the tau/scrolls summ_screen_fd dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1917
- Rouge1: 28.1708
- Rouge2: 6.6895
- Rougel: 18.1637
- Rougelsum: 24.3987
- Gen Len: 96.2041
## 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: 2
- 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: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:--------:|
| 0.4063 | 0.97 | 14 | 3.7385 | 27.9171 | 6.7215 | 17.9315 | 24.363 | 71.9083 |
| 0.3125 | 1.95 | 28 | 3.1917 | 28.1708 | 6.6895 | 18.1637 | 24.3987 | 96.2041 |
| 0.2177 | 2.99 | 43 | 3.9998 | 29.3167 | 5.9 | 17.3608 | 25.6945 | 198.0473 |
| 0.1753 | 3.97 | 57 | 4.2287 | 29.0605 | 6.2534 | 17.5744 | 25.6415 | 158.6509 |
| 0.2747 | 4.94 | 71 | 4.1027 | 31.2245 | 6.5663 | 18.1588 | 26.8996 | 118.4438 |
| 0.1045 | 5.98 | 86 | 5.0581 | 30.6056 | 6.8892 | 18.4933 | 26.4027 | 92.9882 |
| 0.0875 | 6.96 | 100 | 4.5941 | 32.5234 | 7.3736 | 18.8958 | 28.4738 | 160.8964 |
| 0.1572 | 8.0 | 115 | 4.9386 | 31.4658 | 7.2592 | 18.4796 | 27.6047 | 121.0178 |
| 0.0867 | 8.97 | 129 | 4.5565 | 32.0531 | 7.0692 | 18.5551 | 27.3373 | 160.4793 |
| 0.0748 | 9.74 | 140 | 5.0866 | 32.2717 | 7.7004 | 18.9107 | 28.3874 | 124.1893 |
### Framework versions
- Transformers 4.34.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
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