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---
library_name: transformers
license: apache-2.0
base_model: google-t5/t5-base
tags:
- generated_from_trainer
datasets:
- generator
metrics:
- accuracy
model-index:
- name: google_t5_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: 0.6179074697593216
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. -->
# google_t5_language_ID
This model is a fine-tuned version of [google-t5/t5-base](https://huggingface.co/google-t5/t5-base) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5429
- Accuracy: 0.6179
- F1 Macro: 0.3389
- F1 Weighted: 0.5774
- Precision Macro: 0.3873
- Recall Macro: 0.3627
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|:-----------:|:---------------:|:------------:|
| 0.1943 | 0.0083 | 500 | 0.6981 | 0.4018 | 0.3139 | 0.3488 | 0.4624 | 0.3616 |
| 0.0812 | 0.0167 | 1000 | 0.7371 | 0.4086 | 0.3323 | 0.3446 | 0.5179 | 0.3940 |
| 0.049 | 0.025 | 1500 | 0.7806 | 0.4534 | 0.3793 | 0.3793 | 0.5316 | 0.4534 |
| 0.0518 | 0.0333 | 2000 | 0.5042 | 0.5845 | 0.5071 | 0.5258 | 0.5576 | 0.5637 |
| 0.0452 | 0.0417 | 2500 | 0.5120 | 0.6204 | 0.5554 | 0.5554 | 0.6496 | 0.6204 |
| 0.0288 | 0.05 | 3000 | 0.4798 | 0.6018 | 0.5230 | 0.5618 | 0.6077 | 0.5603 |
| 0.0341 | 0.0583 | 3500 | 0.4764 | 0.6098 | 0.5456 | 0.5658 | 0.6528 | 0.5881 |
| 0.0762 | 0.0667 | 4000 | 0.4389 | 0.6251 | 0.5296 | 0.5688 | 0.6091 | 0.5820 |
| 0.0189 | 0.075 | 4500 | 0.4167 | 0.6681 | 0.6068 | 0.6068 | 0.7167 | 0.6681 |
| 0.0235 | 0.0833 | 5000 | 0.4673 | 0.6599 | 0.6018 | 0.6018 | 0.7393 | 0.6599 |
| 0.0274 | 0.0917 | 5500 | 0.3304 | 0.6958 | 0.6102 | 0.6555 | 0.6868 | 0.6478 |
| 0.0198 | 0.1 | 6000 | 0.4752 | 0.6569 | 0.5877 | 0.6095 | 0.7165 | 0.6335 |
| 0.0246 | 0.1083 | 6500 | 0.4657 | 0.6540 | 0.5800 | 0.6015 | 0.6400 | 0.6306 |
| 0.0241 | 0.1167 | 7000 | 0.5429 | 0.6179 | 0.3389 | 0.5774 | 0.3873 | 0.3627 |
### Framework versions
- Transformers 4.57.1
- Pytorch 2.9.0+cu128
- Datasets 4.3.0
- Tokenizers 0.22.1
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