|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- rouge |
|
model-index: |
|
- name: flan-t5-large-stacked-samsum1024-WIP3 |
|
results: [] |
|
--- |
|
|
|
<!-- 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-stacked-samsum1024-WIP3 |
|
|
|
This model is a fine-tuned version of [pszemraj/flan-t5-large-stacked-samsum1024-WIP2](https://huggingface.co/pszemraj/flan-t5-large-stacked-samsum1024-WIP2) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 2.1311 |
|
- Rouge1: 58.1114 |
|
- Rouge2: 29.339 |
|
- Rougel: 44.7611 |
|
- Rougelsum: 54.2823 |
|
- Gen Len: 122.364 |
|
|
|
## 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: 0.0006 |
|
- train_batch_size: 4 |
|
- eval_batch_size: 2 |
|
- seed: 2760 |
|
- distributed_type: multi-GPU |
|
- num_devices: 2 |
|
- gradient_accumulation_steps: 32 |
|
- total_train_batch_size: 256 |
|
- total_eval_batch_size: 4 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: cosine |
|
- lr_scheduler_warmup_ratio: 0.02 |
|
- num_epochs: 2.0 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
|
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| |
|
| 0.1734 | 1.0 | 115 | 1.8751 | 57.9286 | 29.2743 | 44.7181 | 54.2295 | 122.123 | |
|
| 0.1098 | 2.0 | 230 | 2.1311 | 58.1114 | 29.339 | 44.7611 | 54.2823 | 122.364 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.26.0.dev0 |
|
- Pytorch 1.13.0+cu117 |
|
- Datasets 2.6.1 |
|
- Tokenizers 0.13.1 |
|
|
