---
library_name: transformers
license: apache-2.0
base_model: google/t5-efficient-small
tags:
- generated_from_trainer
datasets:
- generator
metrics:
- accuracy
model-index:
- name: t5_efficient_small_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: 0.6577572709259952
      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. -->

# t5_efficient_small_language_ID

This model is a fine-tuned version of [google/t5-efficient-small](https://huggingface.co/google/t5-efficient-small) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4285
- Accuracy: 0.6578
- F1 Macro: 0.5633
- F1 Weighted: 0.6050
- Precision Macro: 0.6452
- Recall Macro: 0.6124

## 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: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 1000
- training_steps: 60000

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1 Macro | F1 Weighted | Precision Macro | Recall Macro |
|:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|:-----------:|:---------------:|:------------:|
| 0.3649        | 0.0083 | 500  | 0.8746          | 0.2458   | 0.1941   | 0.2013      | 0.2757          | 0.2370       |
| 0.1204        | 0.0167 | 1000 | 0.8914          | 0.3442   | 0.2543   | 0.2637      | 0.4155          | 0.3319       |
| 0.0788        | 0.025  | 1500 | 1.0181          | 0.3853   | 0.3001   | 0.3001      | 0.4832          | 0.3853       |
| 0.0771        | 0.0333 | 2000 | 0.5361          | 0.5775   | 0.4982   | 0.5166      | 0.5265          | 0.5569       |
| 0.0737        | 0.0417 | 2500 | 0.6765          | 0.5442   | 0.4678   | 0.4851      | 0.5409          | 0.5248       |
| 0.0399        | 0.05   | 3000 | 0.6103          | 0.5444   | 0.4692   | 0.4866      | 0.5858          | 0.5250       |
| 0.0557        | 0.0583 | 3500 | 0.4436          | 0.6128   | 0.5453   | 0.5655      | 0.6635          | 0.5909       |
| 0.0963        | 0.0667 | 4000 | 0.4755          | 0.6027   | 0.5328   | 0.5526      | 0.6001          | 0.5812       |
| 0.0282        | 0.075  | 4500 | 0.4607          | 0.6347   | 0.5728   | 0.5728      | 0.6121          | 0.6347       |
| 0.0386        | 0.0833 | 5000 | 0.5344          | 0.6501   | 0.5574   | 0.5781      | 0.6186          | 0.6269       |
| 0.0355        | 0.0917 | 5500 | 0.4191          | 0.6575   | 0.5793   | 0.6008      | 0.6199          | 0.6340       |
| 0.0244        | 0.1    | 6000 | 0.4040          | 0.6802   | 0.5880   | 0.6316      | 0.6406          | 0.6333       |
| 0.0331        | 0.1083 | 6500 | 0.4438          | 0.6517   | 0.6053   | 0.6053      | 0.7090          | 0.6517       |
| 0.0224        | 0.1167 | 7000 | 0.4869          | 0.6649   | 0.5878   | 0.6096      | 0.6689          | 0.6412       |
| 0.0263        | 0.125  | 7500 | 0.4285          | 0.6578   | 0.5633   | 0.6050      | 0.6452          | 0.6124       |


### Framework versions

- Transformers 4.57.1
- Pytorch 2.9.0+cu128
- Datasets 4.3.0
- Tokenizers 0.22.1