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t5-turkish-to-english

Text2Text Generation
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t5
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t5-turkish-to-english / README.md
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - wmt16
base_model: t5-base
model-index:
  - name: t5-turkish-to-english
    results: []

t5-turkish-to-english

This model is a fine-tuned version of t5-base on the wmt16 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0282

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: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 3

Training results

Training Loss Epoch Step Validation Loss
0.6168 0.02 500 0.0497
0.0832 0.04 1000 0.0448
0.0791 0.06 1500 0.0424
0.0718 0.08 2000 0.0413
0.0661 0.1 2500 0.0406
0.0669 0.12 3000 0.0399
0.065 0.14 3500 0.0389
0.0627 0.16 4000 0.0389
0.0637 0.17 4500 0.0396
0.0599 0.19 5000 0.0376
0.0601 0.21 5500 0.0368
0.0594 0.23 6000 0.0379
0.0578 0.25 6500 0.0371
0.0577 0.27 7000 0.0383
0.0566 0.29 7500 0.0377
0.0554 0.31 8000 0.0351
0.0554 0.33 8500 0.0347
0.0546 0.35 9000 0.0351
0.0564 0.37 9500 0.0356
0.0533 0.39 10000 0.0340
0.0515 0.41 10500 0.0339
0.0523 0.43 11000 0.0337
0.0528 0.45 11500 0.0337
0.0536 0.47 12000 0.0332
0.0501 0.49 12500 0.0334
0.0493 0.51 13000 0.0332
0.0504 0.52 13500 0.0331
0.0484 0.54 14000 0.0328
0.0496 0.56 14500 0.0327
0.0469 0.58 15000 0.0331
0.0483 0.6 15500 0.0329
0.0477 0.62 16000 0.0326
0.0492 0.64 16500 0.0326
0.0482 0.66 17000 0.0322
0.0468 0.68 17500 0.0323
0.0474 0.7 18000 0.0320
0.0463 0.72 18500 0.0321
0.048 0.74 19000 0.0319
0.0463 0.76 19500 0.0319
0.0467 0.78 20000 0.0316
0.0457 0.8 20500 0.0319
0.0463 0.82 21000 0.0320
0.045 0.84 21500 0.0317
0.0442 0.86 22000 0.0314
0.0462 0.87 22500 0.0313
0.0453 0.89 23000 0.0313
0.0455 0.91 23500 0.0316
0.0459 0.93 24000 0.0311
0.0435 0.95 24500 0.0312
0.0451 0.97 25000 0.0310
0.043 0.99 25500 0.0310
0.0429 1.01 26000 0.0306
0.0423 1.03 26500 0.0309
0.0418 1.05 27000 0.0309
0.0418 1.07 27500 0.0308
0.0414 1.09 28000 0.0307
0.0426 1.11 28500 0.0308
0.0411 1.13 29000 0.0306
0.0414 1.15 29500 0.0310
0.0411 1.17 30000 0.0305
0.0424 1.19 30500 0.0305
0.0419 1.21 31000 0.0307
0.0415 1.22 31500 0.0304
0.0403 1.24 32000 0.0303
0.0411 1.26 32500 0.0302
0.0414 1.28 33000 0.0304
0.0412 1.3 33500 0.0301
0.0404 1.32 34000 0.0304
0.0403 1.34 34500 0.0304
0.0415 1.36 35000 0.0302
0.0389 1.38 35500 0.0303
0.0401 1.4 36000 0.0300
0.0393 1.42 36500 0.0301
0.0399 1.44 37000 0.0297
0.0404 1.46 37500 0.0297
0.0404 1.48 38000 0.0298
0.04 1.5 38500 0.0296
0.0403 1.52 39000 0.0296
0.04 1.54 39500 0.0294
0.0392 1.56 40000 0.0295
0.0392 1.57 40500 0.0295
0.0388 1.59 41000 0.0296
0.0398 1.61 41500 0.0297
0.0388 1.63 42000 0.0293
0.0385 1.65 42500 0.0294
0.0392 1.67 43000 0.0291
0.0384 1.69 43500 0.0293
0.0384 1.71 44000 0.0294
0.0395 1.73 44500 0.0291
0.0391 1.75 45000 0.0293
0.0375 1.77 45500 0.0293
0.0375 1.79 46000 0.0294
0.0388 1.81 46500 0.0292
0.0392 1.83 47000 0.0293
0.0382 1.85 47500 0.0294
0.038 1.87 48000 0.0293
0.0388 1.89 48500 0.0292
0.0383 1.91 49000 0.0290
0.0381 1.92 49500 0.0292
0.0388 1.94 50000 0.0290
0.0378 1.96 50500 0.0289
0.0391 1.98 51000 0.0290
0.0379 2.0 51500 0.0289
0.0364 2.02 52000 0.0289
0.0366 2.04 52500 0.0291
0.0362 2.06 53000 0.0291
0.0359 2.08 53500 0.0289
0.0367 2.1 54000 0.0291
0.0368 2.12 54500 0.0290
0.0359 2.14 55000 0.0288
0.0359 2.16 55500 0.0289
0.036 2.18 56000 0.0289
0.0362 2.2 56500 0.0288
0.0359 2.22 57000 0.0287
0.0374 2.24 57500 0.0287
0.0353 2.26 58000 0.0286
0.0351 2.27 58500 0.0287
0.0348 2.29 59000 0.0286
0.0355 2.31 59500 0.0286
0.0362 2.33 60000 0.0287
0.0361 2.35 60500 0.0287
0.0354 2.37 61000 0.0286
0.036 2.39 61500 0.0284
0.0341 2.41 62000 0.0285
0.0348 2.43 62500 0.0284
0.036 2.45 63000 0.0285
0.0351 2.47 63500 0.0284
0.0354 2.49 64000 0.0284
0.0372 2.51 64500 0.0285
0.035 2.53 65000 0.0285
0.0348 2.55 65500 0.0284
0.0353 2.57 66000 0.0283
0.0353 2.59 66500 0.0283
0.0352 2.6 67000 0.0283
0.0357 2.62 67500 0.0283
0.035 2.64 68000 0.0283
0.0352 2.66 68500 0.0283
0.035 2.68 69000 0.0282
0.0348 2.7 69500 0.0282
0.0344 2.72 70000 0.0281
0.0357 2.74 70500 0.0282
0.0348 2.76 71000 0.0282
0.0349 2.78 71500 0.0281
0.0365 2.8 72000 0.0282
0.0354 2.82 72500 0.0282
0.0359 2.84 73000 0.0281
0.0343 2.86 73500 0.0282
0.0343 2.88 74000 0.0281
0.0346 2.9 74500 0.0282
0.0357 2.92 75000 0.0282
0.0351 2.94 75500 0.0282
0.0355 2.95 76000 0.0282
0.0351 2.97 76500 0.0282
0.0359 2.99 77000 0.0282

Framework versions

  • Transformers 4.20.1
  • Pytorch 1.11.0
  • Datasets 2.1.0
  • Tokenizers 0.12.1