|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- gsm8k |
|
model-index: |
|
- name: flan-t5-base-finetuned-gsm8k |
|
results: [] |
|
widget: |
|
- text: "Please, answer the following question reasoning step-by-step: |
|
Manu bought 4 apples and lost one in the market. How many apples does Manu have?" |
|
--- |
|
|
|
<!-- 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-base-finetuned-gsm8k |
|
|
|
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the gsm8k dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.3652 |
|
- Rouge2 Precision: 0.3914 |
|
- Rouge2 Recall: 0.0816 |
|
- Rouge2 Fmeasure: 0.1308 |
|
|
|
## 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: 5e-05 |
|
- train_batch_size: 4 |
|
- eval_batch_size: 8 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 5 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |
|
|:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:| |
|
| 0.425 | 1.0 | 1869 | 0.3942 | 0.3707 | 0.0774 | 0.1238 | |
|
| 0.3849 | 2.0 | 3738 | 0.3769 | 0.3809 | 0.0795 | 0.1272 | |
|
| 0.3663 | 3.0 | 5607 | 0.3698 | 0.3808 | 0.0805 | 0.1285 | |
|
| 0.3553 | 4.0 | 7476 | 0.3659 | 0.3863 | 0.0805 | 0.129 | |
|
| 0.3421 | 5.0 | 9345 | 0.3652 | 0.3914 | 0.0816 | 0.1308 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.24.0 |
|
- Pytorch 1.12.1+cu113 |
|
- Datasets 2.6.1 |
|
- Tokenizers 0.13.2 |
|
|
