|
--- |
|
license: apache-2.0 |
|
base_model: google/t5-efficient-tiny |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- generator |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: salt_language_ID |
|
results: |
|
- task: |
|
name: Sequence-to-sequence Language Modeling |
|
type: text2text-generation |
|
dataset: |
|
name: generator |
|
type: generator |
|
config: default |
|
split: train |
|
args: default |
|
metrics: |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.9734543010752689 |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# salt_language_ID |
|
|
|
This model is a fine-tuned version of [google/t5-efficient-tiny](https://huggingface.co/google/t5-efficient-tiny) on the generator dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.0158 |
|
- Accuracy: 0.9735 |
|
|
|
## 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: 64 |
|
- eval_batch_size: 64 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- lr_scheduler_warmup_steps: 10 |
|
- training_steps: 20000 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
|
|:-------------:|:-----:|:-----:|:---------------:|:--------:| |
|
| 0.5256 | 0.025 | 500 | 0.1505 | 0.7698 | |
|
| 0.0708 | 0.05 | 1000 | 0.0447 | 0.9237 | |
|
| 0.0547 | 0.075 | 1500 | 0.0540 | 0.9093 | |
|
| 0.0476 | 0.1 | 2000 | 0.0428 | 0.9264 | |
|
| 0.0413 | 0.125 | 2500 | 0.0334 | 0.9399 | |
|
| 0.0404 | 0.15 | 3000 | 0.0293 | 0.9479 | |
|
| 0.0374 | 0.175 | 3500 | 0.0324 | 0.9459 | |
|
| 0.0359 | 0.2 | 4000 | 0.0257 | 0.9493 | |
|
| 0.0353 | 0.225 | 4500 | 0.0267 | 0.9513 | |
|
| 0.0336 | 0.25 | 5000 | 0.0234 | 0.9587 | |
|
| 0.0337 | 0.275 | 5500 | 0.0253 | 0.9560 | |
|
| 0.0324 | 0.3 | 6000 | 0.0186 | 0.9684 | |
|
| 0.0307 | 0.325 | 6500 | 0.0208 | 0.9634 | |
|
| 0.028 | 0.35 | 7000 | 0.0253 | 0.9573 | |
|
| 0.0297 | 0.375 | 7500 | 0.0224 | 0.9617 | |
|
| 0.0264 | 0.4 | 8000 | 0.0224 | 0.9607 | |
|
| 0.027 | 0.425 | 8500 | 0.0185 | 0.9667 | |
|
| 0.0266 | 0.45 | 9000 | 0.0222 | 0.9634 | |
|
| 0.0259 | 0.475 | 9500 | 0.0221 | 0.9617 | |
|
| 0.0244 | 0.5 | 10000 | 0.0187 | 0.9688 | |
|
| 0.0243 | 0.525 | 10500 | 0.0164 | 0.9694 | |
|
| 0.0248 | 0.55 | 11000 | 0.0184 | 0.9674 | |
|
| 0.024 | 0.575 | 11500 | 0.0155 | 0.9704 | |
|
| 0.0228 | 0.6 | 12000 | 0.0176 | 0.9671 | |
|
| 0.0241 | 0.625 | 12500 | 0.0146 | 0.9755 | |
|
| 0.0234 | 0.65 | 13000 | 0.0181 | 0.9681 | |
|
| 0.0226 | 0.675 | 13500 | 0.0142 | 0.9758 | |
|
| 0.0225 | 0.7 | 14000 | 0.0169 | 0.9718 | |
|
| 0.0218 | 0.725 | 14500 | 0.0151 | 0.9711 | |
|
| 0.0212 | 0.75 | 15000 | 0.0176 | 0.9735 | |
|
| 0.0199 | 0.775 | 15500 | 0.0131 | 0.9741 | |
|
| 0.0208 | 0.8 | 16000 | 0.0131 | 0.9775 | |
|
| 0.0217 | 0.825 | 16500 | 0.0123 | 0.9788 | |
|
| 0.0208 | 0.85 | 17000 | 0.0145 | 0.9758 | |
|
| 0.0217 | 0.875 | 17500 | 0.0154 | 0.9694 | |
|
| 0.0197 | 0.9 | 18000 | 0.0138 | 0.9765 | |
|
| 0.0205 | 0.925 | 18500 | 0.0138 | 0.9748 | |
|
| 0.0203 | 0.95 | 19000 | 0.0146 | 0.9748 | |
|
| 0.0198 | 0.975 | 19500 | 0.0131 | 0.9755 | |
|
| 0.0204 | 1.0 | 20000 | 0.0158 | 0.9735 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.40.2 |
|
- Pytorch 2.2.1+cu121 |
|
- Datasets 2.19.1 |
|
- Tokenizers 0.19.1 |
|
|
