---
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: 0.980510752688172
---

<!-- 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