---
library_name: transformers
license: apache-2.0
base_model: google/t5-efficient-tiny
tags:
- generated_from_trainer
datasets:
- generator
metrics:
- accuracy
model-index:
- name: sunflower_language_ID_improved
  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: 0.6293109420681438
      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. -->

# sunflower_language_ID_improved

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: 0.5044
- Accuracy: 0.6293
- F1 Macro: 0.5576
- F1 Weighted: 0.5783
- Precision Macro: 0.6310
- Recall Macro: 0.6068

## 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 OptimizerNames.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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|:-----------:|:---------------:|:------------:|
| 0.8129        | 0.0083 | 500  | 0.9712          | 0.0998   | 0.0544   | 0.0564      | 0.0925          | 0.0963       |
| 0.1835        | 0.0167 | 1000 | 0.9716          | 0.2110   | 0.1089   | 0.1089      | 0.1382          | 0.2110       |
| 0.1376        | 0.025  | 1500 | 1.1180          | 0.2453   | 0.1461   | 0.1515      | 0.2733          | 0.2365       |
| 0.1637        | 0.0333 | 2000 | 0.5585          | 0.4419   | 0.3848   | 0.3991      | 0.4617          | 0.4261       |
| 0.1382        | 0.0417 | 2500 | 0.6304          | 0.4811   | 0.4199   | 0.4355      | 0.5272          | 0.4639       |
| 0.0589        | 0.05   | 3000 | 0.7011          | 0.4349   | 0.3593   | 0.3726      | 0.4607          | 0.4194       |
| 0.1073        | 0.0583 | 3500 | 0.5442          | 0.4991   | 0.4470   | 0.4470      | 0.5804          | 0.4991       |
| 0.1461        | 0.0667 | 4000 | 0.4705          | 0.5609   | 0.4802   | 0.4980      | 0.5335          | 0.5408       |
| 0.059         | 0.075  | 4500 | 0.5019          | 0.5684   | 0.4987   | 0.4987      | 0.6235          | 0.5684       |
| 0.06          | 0.0833 | 5000 | 0.5568          | 0.6106   | 0.5485   | 0.5485      | 0.5973          | 0.6106       |
| 0.0617        | 0.0917 | 5500 | 0.4218          | 0.6231   | 0.5450   | 0.5651      | 0.5866          | 0.6008       |
| 0.0458        | 0.1    | 6000 | 0.4697          | 0.6276   | 0.5773   | 0.5773      | 0.6620          | 0.6276       |
| 0.0646        | 0.1083 | 6500 | 0.4356          | 0.6173   | 0.5432   | 0.5633      | 0.6516          | 0.5952       |
| 0.0447        | 0.1167 | 7000 | 0.4705          | 0.6358   | 0.5978   | 0.5978      | 0.6953          | 0.6358       |
| 0.0384        | 0.125  | 7500 | 0.4685          | 0.6173   | 0.5600   | 0.5600      | 0.6539          | 0.6173       |
| 0.0398        | 0.1333 | 8000 | 0.4796          | 0.6430   | 0.5722   | 0.5933      | 0.6100          | 0.6201       |
| 0.0323        | 0.1417 | 8500 | 0.6236          | 0.5705   | 0.5191   | 0.5191      | 0.5960          | 0.5705       |
| 0.0344        | 0.15   | 9000 | 0.4619          | 0.6296   | 0.5962   | 0.5962      | 0.7179          | 0.6296       |
| 0.0458        | 0.1583 | 9500 | 0.5044          | 0.6293   | 0.5576   | 0.5783      | 0.6310          | 0.6068       |


### Framework versions

- Transformers 4.57.1
- Pytorch 2.8.0+cu126
- Datasets 4.4.1
- Tokenizers 0.22.1