t5_efficient_small_language_ID

yigagilbert/t5_efficient_small_language_ID

t5_efficient_small_language_ID 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 14, 2025
Updated: Nov 14, 2025
SHA: 3fec0ac9

Base models

google/t5-efficient-small

Datasets

generator

Model card

From the Hugging Face model README

t5_efficient_small_language_ID

This model is a fine-tuned version of 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

Library: transformers
Type: t5
License: apache-2.0
Created: Nov 14, 2025
Updated: Nov 14, 2025