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---
license: apache-2.0
tags:
- generated_from_trainer
base_model: google/t5-efficient-tiny
datasets:
- generator
metrics:
- accuracy
model-index:
- name: salt_language_ID
results:
- task:
type: text2text-generation
name: Sequence-to-sequence Language Modeling
dataset:
name: generator
type: generator
config: default
split: train
args: default
metrics:
- type: accuracy
value: 0.980510752688172
name: Accuracy
---
<!-- 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: 0.0127
- Accuracy: 0.9805
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5069 | 0.025 | 500 | 0.1145 | 0.8337 |
| 0.0644 | 0.05 | 1000 | 0.0489 | 0.9170 |
| 0.0511 | 0.075 | 1500 | 0.0605 | 0.9056 |
| 0.0462 | 0.1 | 2000 | 0.0332 | 0.9432 |
| 0.0411 | 0.125 | 2500 | 0.0358 | 0.9385 |
| 0.0409 | 0.15 | 3000 | 0.0267 | 0.9509 |
| 0.0365 | 0.175 | 3500 | 0.0244 | 0.9563 |
| 0.0359 | 0.2 | 4000 | 0.0285 | 0.9536 |
| 0.035 | 0.225 | 4500 | 0.0355 | 0.9388 |
| 0.0321 | 0.25 | 5000 | 0.0264 | 0.9570 |
| 0.0327 | 0.275 | 5500 | 0.0278 | 0.9513 |
| 0.0313 | 0.3 | 6000 | 0.0217 | 0.9630 |
| 0.0305 | 0.325 | 6500 | 0.0255 | 0.9556 |
| 0.0285 | 0.35 | 7000 | 0.0187 | 0.9630 |
| 0.0293 | 0.375 | 7500 | 0.0225 | 0.9620 |
| 0.0264 | 0.4 | 8000 | 0.0228 | 0.9614 |
| 0.0272 | 0.425 | 8500 | 0.0195 | 0.9664 |
| 0.0268 | 0.45 | 9000 | 0.0178 | 0.9688 |
| 0.0259 | 0.475 | 9500 | 0.0164 | 0.9677 |
| 0.0256 | 0.5 | 10000 | 0.0167 | 0.9721 |
| 0.0241 | 0.525 | 10500 | 0.0182 | 0.9647 |
| 0.0235 | 0.55 | 11000 | 0.0212 | 0.9657 |
| 0.0239 | 0.575 | 11500 | 0.0145 | 0.9735 |
| 0.0239 | 0.6 | 12000 | 0.0173 | 0.9704 |
| 0.0234 | 0.625 | 12500 | 0.0152 | 0.9768 |
| 0.0229 | 0.65 | 13000 | 0.0181 | 0.9698 |
| 0.023 | 0.675 | 13500 | 0.0154 | 0.9735 |
| 0.0224 | 0.7 | 14000 | 0.0157 | 0.9708 |
| 0.0221 | 0.725 | 14500 | 0.0155 | 0.9714 |
| 0.0219 | 0.75 | 15000 | 0.0145 | 0.9755 |
| 0.0213 | 0.775 | 15500 | 0.0159 | 0.9735 |
| 0.0197 | 0.8 | 16000 | 0.0129 | 0.9751 |
| 0.0206 | 0.825 | 16500 | 0.0154 | 0.9724 |
| 0.02 | 0.85 | 17000 | 0.0140 | 0.9724 |
| 0.0209 | 0.875 | 17500 | 0.0115 | 0.9772 |
| 0.0191 | 0.9 | 18000 | 0.0129 | 0.9735 |
| 0.0194 | 0.925 | 18500 | 0.0120 | 0.9765 |
| 0.0191 | 0.95 | 19000 | 0.0133 | 0.9741 |
| 0.0183 | 0.975 | 19500 | 0.0166 | 0.9731 |
| 0.0207 | 1.0 | 20000 | 0.0127 | 0.9805 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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