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t5-large_cola_sp0_ar0

Text Classification
Transformers
PyTorch
t5
Generated from Trainer
Eval Results
text-generation-inference
Inference Endpoints
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t5-large_cola_sp0_ar0 / README.md
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metadata
license: apache-2.0
base_model: t5-large
tags:
  - generated_from_trainer
datasets:
  - glue
metrics:
  - accuracy
model-index:
  - name: t5-large_cola_sp0_ar0
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: glue
          type: glue
          config: cola
          split: validation
          args: cola
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.880859375

t5-large_cola_sp0_ar0

This model is a fine-tuned version of t5-large on the glue dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4179
  • Accuracy: 0.8809

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: 5e-05
  • train_batch_size: 16
  • eval_batch_size: 32
  • seed: 1
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 20
  • num_epochs: 6

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.5885 0.05 25 0.6751 0.6913
0.5475 0.11 50 0.5338 0.6913
0.5122 0.16 75 0.4847 0.7919
0.4486 0.21 100 0.5089 0.7996
0.4087 0.27 125 0.5139 0.8063
0.4022 0.32 150 0.5188 0.8035
0.4245 0.37 175 0.5196 0.7987
0.4298 0.42 200 0.6226 0.8006
0.4326 0.48 225 0.6169 0.8015
0.4321 0.53 250 0.6173 0.7987
0.4288 0.58 275 0.4786 0.8102
0.3914 0.64 300 0.5147 0.8054
0.3519 0.69 325 0.5691 0.8150
0.4036 0.74 350 0.4560 0.8236
0.3706 0.8 375 0.4640 0.8245
0.3584 0.85 400 0.4605 0.8207
0.3539 0.9 425 0.4932 0.8217
0.3982 0.96 450 0.5397 0.8073
0.3352 1.01 475 0.5490 0.8150
0.2631 1.06 500 0.9244 0.8121
0.2992 1.11 525 0.5666 0.8169
0.2308 1.17 550 0.7285 0.8178
0.2893 1.22 575 0.6907 0.8198
0.2809 1.27 600 0.4998 0.8140
0.2469 1.33 625 0.7260 0.8236
0.331 1.38 650 0.5812 0.8293
0.286 1.43 675 0.5102 0.8360
0.347 1.49 700 0.5696 0.8255
0.2971 1.54 725 0.4114 0.8380
0.3048 1.59 750 0.5466 0.8169
0.3168 1.65 775 0.4787 0.8274
0.2247 1.7 800 0.7926 0.8063
0.2666 1.75 825 0.5763 0.8274
0.2856 1.8 850 0.5131 0.8303
0.2967 1.86 875 0.4970 0.8293
0.296 1.91 900 0.5532 0.8293
0.2828 1.96 925 0.4777 0.8274
0.2708 2.02 950 0.5433 0.8351
0.1406 2.07 975 0.6351 0.8351
0.2046 2.12 1000 0.6058 0.8332
0.2227 2.18 1025 0.5616 0.8408
0.1551 2.23 1050 1.0299 0.8360
0.1465 2.28 1075 0.7842 0.8380
0.2171 2.34 1100 0.6329 0.8437
0.1588 2.39 1125 0.7575 0.8418
0.4245 2.44 1150 0.7603 0.8351
0.2124 2.49 1175 0.5838 0.8447
0.2333 2.55 1200 0.4896 0.8418
0.1943 2.6 1225 0.6343 0.8332
0.1961 2.65 1250 0.6343 0.8284
0.1981 2.71 1275 0.6145 0.8332
0.2151 2.76 1300 0.6335 0.8360
0.1634 2.81 1325 1.1357 0.8399
0.1526 2.87 1350 1.0044 0.8293
0.2096 2.92 1375 0.7761 0.8360
0.2135 2.97 1400 0.9338 0.8351
0.155 3.03 1425 3.3297 0.8360
0.3667 3.08 1450 4.0564 0.8370
0.5925 3.13 1475 6.7411 0.8408
0.5866 3.18 1500 7.1940 0.8399
0.3812 3.24 1525 7.0097 0.8351
0.1041 3.29 1550 7.0157 0.8351
0.3451 3.34 1575 6.2653 0.8418
0.1121 3.4 1600 4.2608 0.8485

Framework versions

  • Transformers 4.33.2
  • Pytorch 2.0.1+cu117
  • Datasets 2.14.5
  • Tokenizers 0.11.6