Pictalk_large / README.md
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
base_model: bert-large-uncased
model-index:
  - name: pictalk
    results: []

pictalk

This model is a fine-tuned version of bert-large-uncased on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 1.3395

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: 1e-05
  • train_batch_size: 128
  • eval_batch_size: 128
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 50
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss
4.4213 1.0 25 3.2802
3.1204 2.0 50 2.8289
2.7337 3.0 75 2.5070
2.4701 4.0 100 2.1833
2.2536 5.0 125 2.0859
2.1284 6.0 150 2.0973
1.9703 7.0 175 1.8079
1.9372 8.0 200 1.8733
1.9115 9.0 225 1.7319
1.7705 10.0 250 1.8154
1.7454 11.0 275 1.6135
1.7338 12.0 300 1.6072
1.6741 13.0 325 1.4479
1.6552 14.0 350 1.6893
1.5546 15.0 375 1.5714
1.5905 16.0 400 1.6661
1.5136 17.0 425 1.6100
1.5403 18.0 450 1.5664
1.4947 19.0 475 1.4803
1.4654 20.0 500 1.6041
1.4449 21.0 525 1.4071
1.4817 22.0 550 1.5543
1.377 23.0 575 1.3897
1.4102 24.0 600 1.4572
1.3246 25.0 625 1.5699
1.3323 26.0 650 1.4316
1.2745 27.0 675 1.5004
1.2589 28.0 700 1.5209
1.3488 29.0 725 1.4734
1.301 30.0 750 1.5197
1.2824 31.0 775 1.5087
1.2771 32.0 800 1.4041
1.2794 33.0 825 1.5773
1.2343 34.0 850 1.3722
1.3235 35.0 875 1.5125
1.2567 36.0 900 1.3877
1.2682 37.0 925 1.5471
1.2028 38.0 950 1.3677
1.2059 39.0 975 1.4233
1.2103 40.0 1000 1.5361
1.1987 41.0 1025 1.5492
1.2853 42.0 1050 1.4274
1.2088 43.0 1075 1.5027
1.2573 44.0 1100 1.5138
1.2511 45.0 1125 1.4198
1.1932 46.0 1150 1.3065
1.1864 47.0 1175 1.4521
1.2362 48.0 1200 1.4576
1.215 49.0 1225 1.4246
1.2118 50.0 1250 1.3395

Framework versions

  • Transformers 4.35.2
  • Pytorch 2.1.0+cu121
  • Datasets 2.16.1
  • Tokenizers 0.15.0