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osbm
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t5-turkish-to-english

Text2Text Generation
Transformers
PyTorch
TensorBoard
t5
Generated from Trainer
text-generation-inference
Inference Endpoints
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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
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Dataset used to train osbm/t5-turkish-to-english

Evaluation results

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