sunflower_language_ID_improved

yigagilbert/sunflower_language_ID_improved

sunflower_language_ID_improved is a machine learning model from yigagilbert. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

Library: transformers
License: apache-2.0
Model type: t5
Access: Public
Created: Nov 10, 2025
Updated: Nov 10, 2025
SHA: 6109cf6d

Base models

google/t5-efficient-tiny

Datasets

generator

Model card

From the Hugging Face model README

sunflower_language_ID_improved

This model is a fine-tuned version of 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

Library: transformers
Type: t5
License: apache-2.0
Created: Nov 10, 2025
Updated: Nov 10, 2025