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---
library_name: transformers
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: 0.608582394590625
---
<!-- 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: 0.4200
- Accuracy: 0.6086
## 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: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- training_steps: 20000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9948 | 0.025 | 500 | 0.7153 | 0.1757 |
| 0.3269 | 0.05 | 1000 | 0.7217 | 0.2611 |
| 0.2853 | 0.075 | 1500 | 0.9151 | 0.2412 |
| 0.1823 | 0.1 | 2000 | 0.5561 | 0.3965 |
| 0.1953 | 0.125 | 2500 | 0.5975 | 0.3824 |
| 0.1831 | 0.15 | 3000 | 0.5670 | 0.4264 |
| 0.141 | 0.175 | 3500 | 0.7885 | 0.3443 |
| 0.1081 | 0.2 | 4000 | 0.8961 | 0.3111 |
| 0.154 | 0.225 | 4500 | 0.7975 | 0.3491 |
| 0.1306 | 0.25 | 5000 | 0.4824 | 0.5092 |
| 0.1013 | 0.275 | 5500 | 0.4946 | 0.4613 |
| 0.1083 | 0.3 | 6000 | 0.6959 | 0.4038 |
| 0.1121 | 0.325 | 6500 | 0.6938 | 0.4004 |
| 0.1168 | 0.35 | 7000 | 0.7787 | 0.3948 |
| 0.1202 | 0.375 | 7500 | 0.5420 | 0.4975 |
| 0.1169 | 0.4 | 8000 | 0.5099 | 0.5128 |
| 0.1119 | 0.425 | 8500 | 0.5815 | 0.4582 |
| 0.1258 | 0.45 | 9000 | 0.5103 | 0.5002 |
| 0.0878 | 0.475 | 9500 | 0.5189 | 0.5089 |
| 0.1032 | 0.5 | 10000 | 0.4365 | 0.5674 |
| 0.0854 | 0.525 | 10500 | 0.5854 | 0.5176 |
| 0.1028 | 0.55 | 11000 | 0.5167 | 0.5253 |
| 0.0853 | 0.575 | 11500 | 0.4268 | 0.5922 |
| 0.0716 | 0.6 | 12000 | 0.5486 | 0.5204 |
| 0.0771 | 0.625 | 12500 | 0.4643 | 0.5532 |
| 0.0613 | 0.65 | 13000 | 0.5525 | 0.5050 |
| 0.0819 | 0.675 | 13500 | 0.4500 | 0.5953 |
| 0.0785 | 0.7 | 14000 | 0.5016 | 0.5245 |
| 0.079 | 0.725 | 14500 | 0.4453 | 0.5789 |
| 0.0749 | 0.75 | 15000 | 0.4218 | 0.5866 |
| 0.0749 | 0.775 | 15500 | 0.4208 | 0.6114 |
| 0.0655 | 0.8 | 16000 | 0.4203 | 0.6133 |
| 0.077 | 0.825 | 16500 | 0.4446 | 0.5891 |
| 0.0516 | 0.85 | 17000 | 0.4239 | 0.5985 |
| 0.0555 | 0.875 | 17500 | 0.4040 | 0.6237 |
| 0.0622 | 0.9 | 18000 | 0.4575 | 0.5978 |
| 0.0752 | 0.925 | 18500 | 0.4257 | 0.5959 |
| 0.0555 | 0.95 | 19000 | 0.4462 | 0.5997 |
| 0.0646 | 0.975 | 19500 | 0.4225 | 0.6124 |
| 0.0676 | 1.0 | 20000 | 0.4200 | 0.6086 |
### Framework versions
- Transformers 4.57.1
- Pytorch 2.8.0+cu126
- Datasets 4.4.0
- Tokenizers 0.22.1
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