File size: 1,959 Bytes
3aa80d6 ec8ddc3 8d92e93 3aa80d6 ec8ddc3 da9feed ec8ddc3 da9feed ec8ddc3 64f148c ec8ddc3 da9feed ec8ddc3 da9feed ec8ddc3 da9feed ec8ddc3 da9feed ec8ddc3 8d92e93 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 |
---
license: apache-2.0
datasets:
- stacked-summaries/stacked-samsum-1024
language:
- en
metrics:
- rouge
tags:
- stacked summaries
- samsum
pipeline_tag: summarization
---
# flan-t5-small-stacked-samsum-1024
This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the `stacked-summaries/stacked-samsum-1024` dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7573
- Rouge1: 46.6072
- Rouge2: 19.9754
- Rougel: 35.2715
- Rougelsum: 43.3599
- Gen Len: 72.64
## Model Description
Trained on a summarization task with _potentially_ multiple doc-summary pairs stacked on top of each other.
You can separate its predictions by using it's special token `[NEXT_CONCEPT]` to split the output into "separate topics".
## Intended use & limitations
- This is intended to be used as a baseline/reference for comparison with the larger models.
## Training and evaluation data
See `stacked-summaries/stacked-samsum-1024`.
## Training Procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 22138
- distributed_type: multi-GPU
- gradient_accumulation_steps: 8
- 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: 0.05
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.9011 | 1.0 | 230 | 1.7986 | 45.4597 | 19.6956 | 34.6878 | 42.3724 | 74.16 |
| 1.8297 | 2.0 | 460 | 1.7609 | 46.0427 | 20.2299 | 35.2076 | 43.0549 | 70.56 |
| 1.7637 | 3.0 | 690 | 1.7573 | 46.6072 | 19.9754 | 35.2715 | 43.3599 | 72.64 | |