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---
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
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