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---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- samsum
metrics:
- rouge
pipeline_tag: summarization
base_model: google/flan-t5-large
model-index:
- name: stacked-summaries/flan-t5-large-samsum
results:
- task:
type: summarization
name: Summarization
dataset:
name: samsum
type: samsum
config: samsum
split: test
metrics:
- type: rouge
value: 49.0095
name: ROUGE-1
verified: true
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- type: rouge
value: 25.681
name: ROUGE-2
verified: true
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- type: rouge
value: 41.4474
name: ROUGE-L
verified: true
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- type: rouge
value: 45.1556
name: ROUGE-LSUM
verified: true
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- type: loss
value: 1.2201015949249268
name: loss
verified: true
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- type: gen_len
value: 17.326
name: gen_len
verified: true
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---
<!-- 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. -->
# flan-t5-large-samsum
This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1754
- Rouge1: 54.1595
- Rouge2: 29.1081
- Rougel: 45.4989
- Rougelsum: 49.1026
- Gen Len: 28.72
> Note: the stacked version of this model technically does evaluation on a **different** validation set (the stacked one) while this just uses `samsum`.
## Model description
More information needed
## Intended uses & limitations
- Intended for comparison(s) to the [stacked version of this model](https://huggingface.co/stacked-summaries/flan-t5-large-stacked-samsum-1024)
- 1024 token input max
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 4
- seed: 17868
- distributed_type: multi-GPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.04
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.2106 | 0.43 | 50 | 1.1889 | 52.5898 | 26.9967 | 43.6944 | 47.9656 | 24.5167 |
| 1.213 | 0.87 | 100 | 1.1760 | 52.4279 | 27.4689 | 43.7873 | 48.0581 | 25.0533 |
| 1.0726 | 1.3 | 150 | 1.1731 | 52.8246 | 26.9524 | 43.7429 | 48.0345 | 25.55 |
| 1.0784 | 1.74 | 200 | 1.1708 | 53.1291 | 27.9056 | 44.2609 | 48.6883 | 26.03 |
| 1.0215 | 2.17 | 250 | 1.1755 | 53.1512 | 27.9475 | 44.1442 | 48.4619 | 27.57 |
| 1.0294 | 2.61 | 300 | 1.1711 | 53.4402 | 28.0126 | 44.5454 | 48.6432 | 25.9033 |
| 1.0016 | 3.04 | 350 | 1.1718 | 53.9395 | 28.3087 | 45.191 | 49.2773 | 26.6133 |
| 0.9576 | 3.48 | 400 | 1.1741 | 53.9004 | 28.3243 | 45.0911 | 48.9182 | 26.33 |
| 0.9739 | 3.91 | 450 | 1.1754 | 53.7049 | 28.419 | 44.8946 | 48.8708 | 27.2433 |
| 0.9505 | 4.35 | 500 | 1.1781 | 53.7142 | 28.1758 | 44.8324 | 48.9498 | 26.8667 |
| 0.9993 | 4.78 | 550 | 1.1784 | 53.87 | 28.2211 | 44.893 | 49.1074 | 27.2167 |
|