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sunflower_language_classification

Text Classification
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Safetensors
t5
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metadata 
library_name: transformers
license: apache-2.0
base_model: google/t5-efficient-tiny
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: sunflower_language_classification_v1
    results: []

sunflower_language_classification_v1

This model is a fine-tuned version of google/t5-efficient-tiny on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7212
  • Accuracy: 0.8297
  • Precision: 0.8471
  • Recall: 0.8297
  • F1: 0.8191

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: 0.001
  • train_batch_size: 64
  • eval_batch_size: 64
  • seed: 42
  • 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: linear
  • lr_scheduler_warmup_steps: 10
  • training_steps: 30000

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Recall F1
2.3995 0.0167 500 2.0015 0.5145 0.4412 0.5145 0.4517
1.3282 0.0334 1000 1.6467 0.5688 0.4908 0.5688 0.5080
1.1086 0.0502 1500 1.5051 0.6304 0.5784 0.6304 0.5766
0.9882 0.0669 2000 1.4518 0.6268 0.6374 0.6268 0.5891
0.9187 0.0836 2500 1.3470 0.6522 0.6245 0.6522 0.6093
0.8546 0.1003 3000 1.3747 0.6159 0.5871 0.6159 0.5760
0.8214 0.1170 3500 1.2708 0.6703 0.6316 0.6703 0.6323
0.7843 0.1338 4000 1.1659 0.6848 0.6639 0.6848 0.6461
0.7470 0.1505 4500 1.1969 0.6848 0.6534 0.6848 0.6491
0.7299 0.1672 5000 1.0592 0.7101 0.7030 0.7101 0.6748
0.7041 0.1839 5500 1.0536 0.6848 0.6728 0.6848 0.6534
0.6755 0.2006 6000 1.0265 0.7138 0.7298 0.7138 0.6852
0.6683 0.2174 6500 1.0049 0.7428 0.7403 0.7428 0.7089
0.6573 0.2341 7000 1.0702 0.7029 0.7052 0.7029 0.6764
0.6372 0.2508 7500 1.0260 0.7210 0.7143 0.7210 0.6998
0.6173 0.2675 8000 0.9654 0.7428 0.7492 0.7428 0.7141
0.6009 0.2842 8500 1.0185 0.7464 0.7504 0.7464 0.7167
0.5924 0.3010 9000 1.0028 0.7283 0.7652 0.7283 0.7052
0.5916 0.3177 9500 0.9581 0.7174 0.7217 0.7174 0.6893
0.5806 0.3344 10000 1.0011 0.7355 0.7618 0.7355 0.7149
0.5672 0.3511 10500 0.8978 0.7572 0.7429 0.7572 0.7307
0.5580 0.3678 11000 0.9525 0.7210 0.7308 0.7210 0.7013
0.5520 0.3846 11500 0.8647 0.7645 0.7695 0.7645 0.7391
0.5552 0.4013 12000 0.8977 0.7536 0.7698 0.7536 0.7358
0.5341 0.4180 12500 0.8526 0.7536 0.7625 0.7536 0.7305
0.5284 0.4347 13000 0.8496 0.7464 0.7310 0.7464 0.7166
0.5322 0.4514 13500 0.7672 0.8007 0.8006 0.8007 0.7827
0.5229 0.4681 14000 0.8253 0.7754 0.7698 0.7754 0.7515
0.5007 0.4849 14500 0.8496 0.7826 0.7649 0.7826 0.7547
0.5109 0.5016 15000 0.7700 0.7754 0.7767 0.7754 0.7518
0.4989 0.5183 15500 0.8338 0.7645 0.7741 0.7645 0.7419
0.4991 0.5350 16000 0.7927 0.7754 0.7928 0.7754 0.7625
0.4977 0.5517 16500 0.7859 0.7790 0.7670 0.7790 0.7551
0.4854 0.5685 17000 0.7915 0.7862 0.7907 0.7862 0.7630
0.4826 0.5852 17500 0.7628 0.8043 0.7964 0.8043 0.7846
0.4765 0.6019 18000 0.7632 0.7971 0.8008 0.7971 0.7791
0.4641 0.6186 18500 0.7722 0.7935 0.7660 0.7935 0.7670
0.4783 0.6353 19000 0.7046 0.7899 0.8111 0.7899 0.7773
0.4745 0.6521 19500 0.7342 0.7899 0.8044 0.7899 0.7726
0.4555 0.6688 20000 0.7116 0.7862 0.7853 0.7862 0.7662
0.4530 0.6855 20500 0.7385 0.7754 0.7658 0.7754 0.7557
0.4565 0.7022 21000 0.7651 0.7899 0.8132 0.7899 0.7770
0.4555 0.7189 21500 0.7902 0.7681 0.7812 0.7681 0.7569
0.4485 0.7357 22000 0.7613 0.7862 0.7962 0.7862 0.7686
0.4518 0.7524 22500 0.7544 0.7862 0.7944 0.7862 0.7676
0.4508 0.7691 23000 0.7296 0.8043 0.8110 0.8043 0.7907
0.4418 0.7858 23500 0.7293 0.8261 0.8527 0.8261 0.8137
0.4365 0.8025 24000 0.7370 0.8043 0.8217 0.8043 0.7928
0.4353 0.8193 24500 0.7100 0.8188 0.8274 0.8188 0.8049
0.4240 0.8360 25000 0.7273 0.7862 0.7857 0.7862 0.7697
0.4205 0.8527 25500 0.7297 0.8225 0.8351 0.8225 0.8059
0.4316 0.8694 26000 0.7204 0.8116 0.8066 0.8116 0.7911
0.4176 0.8861 26500 0.7340 0.8080 0.8184 0.8080 0.7922
0.4240 0.9029 27000 0.7298 0.8116 0.8223 0.8116 0.7964
0.4149 0.9196 27500 0.7410 0.8188 0.8185 0.8188 0.8023
0.4159 0.9363 28000 0.7303 0.8152 0.8388 0.8152 0.8069
0.4068 0.9530 28500 0.7220 0.8043 0.8209 0.8043 0.7955
0.4135 0.9697 29000 0.7313 0.8188 0.8238 0.8188 0.8055
0.4130 0.9865 29500 0.7221 0.8225 0.8320 0.8225 0.8095
0.4213 1.0032 30000 0.7212 0.8297 0.8471 0.8297 0.8191

Framework versions

  • Transformers 5.8.0
  • Pytorch 2.11.0+cu130
  • Datasets 4.8.5
  • Tokenizers 0.22.2