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deabuse

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metadata
base_model: ai-forever/ruT5-base
tags:
  - generated_from_trainer
model-index:
  - name: deabuse
    results: []

deabuse

Model description

Модель для блюра матерных слов в предложениях, дообученная модель ai-forever/ruT5-base обучалась на искусственном датасете 40к записей. Полноценно текст блюрит не совсем хорошо, однако по отдельности слова вроде неплохо.

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 2e-05
  • train_batch_size: 1
  • eval_batch_size: 1
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 4
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 1
  • num_epochs: 3

Training results

Training Loss Epoch Step Validation Loss
5.7858 0.05 400 0.7427
4.3608 0.1 800 0.4047
3.2919 0.15 1200 0.2366
3.2244 0.2 1600 0.2164
3.2491 0.25 2000 0.1757
1.6803 0.3 2400 0.1494
3.2828 0.35 2800 0.1500
3.39 0.4 3200 0.1510
0.0933 0.45 3600 0.1524
3.4757 0.5 4000 0.1423
3.1424 0.55 4400 0.1460
0.9616 0.6 4800 0.1178
2.6271 0.65 5200 0.1178
1.1441 0.7 5600 0.1190
3.018 0.75 6000 0.1136
1.3421 0.8 6400 0.0936
2.3062 0.85 6800 0.0994
2.5594 0.9 7200 0.0945
2.1381 0.95 7600 0.1061
1.0893 1.0 8000 0.1029
0.7525 1.05 8400 0.0978
2.1886 1.1 8800 0.0840
1.9948 1.15 9200 0.0952
0.7933 1.2 9600 0.0871
2.0757 1.25 10000 0.0853
0.6129 1.31 10400 0.0857
0.1338 1.36 10800 0.0936
2.6454 1.41 11200 0.0834
0.4243 1.46 11600 0.0891
0.6615 1.51 12000 0.0885
0.6634 1.56 12400 0.0942
0.5665 1.61 12800 0.0808
0.6661 1.66 13200 0.1021
1.1028 1.71 13600 0.0820
1.5217 1.76 14000 0.0769
0.7644 1.81 14400 0.0771
1.3725 1.86 14800 0.0800
0.846 1.91 15200 0.0788
1.7207 1.96 15600 0.0806
0.9188 2.01 16000 0.0806
1.4303 2.06 16400 0.0814
0.1599 2.11 16800 0.1072
0.1976 2.16 17200 0.0823
0.7077 2.21 17600 0.0830
1.8896 2.26 18000 0.0768
0.6957 2.31 18400 0.0826
0.7827 2.36 18800 0.0802
1.3298 2.41 19200 0.0791
0.2254 2.46 19600 0.0871
1.041 2.51 20000 0.0809
1.5451 2.56 20400 0.0838
1.6318 2.61 20800 0.0801
1.8972 2.66 21200 0.0774
1.8895 2.71 21600 0.0762
0.7721 2.76 22000 0.0740
0.3528 2.81 22400 0.0781
1.325 2.86 22800 0.0770
0.0282 2.91 23200 0.0785
1.6303 2.96 23600 0.0760

Framework versions

  • Transformers 4.39.0.dev0
  • Pytorch 2.2.1
  • Datasets 2.16.1
  • Tokenizers 0.15.2