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bhaskara

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PyTorch
gpt_neo
Generated from Trainer
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bhaskara / README.md
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: output
    results: []

output

This model is a fine-tuned version of EleutherAI/gpt-neo-2.7B on the Lila dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5884
  • Accuracy: 0.8664

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: 4
  • eval_batch_size: 4
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 2
  • total_train_batch_size: 8
  • total_eval_batch_size: 8
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10.0

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 0.06 100 0.7930 0.8214
No log 0.11 200 0.7544 0.8290
No log 0.17 300 0.7358 0.8328
No log 0.23 400 0.7192 0.8357
0.8156 0.28 500 0.7012 0.8397
0.8156 0.34 600 0.6904 0.8419
0.8156 0.4 700 0.6802 0.8440
0.8156 0.45 800 0.6670 0.8465
0.8156 0.51 900 0.6572 0.8486
0.7219 0.57 1000 0.6499 0.8500
0.7219 0.62 1100 0.6411 0.8522
0.7219 0.68 1200 0.6343 0.8537
0.7219 0.74 1300 0.6299 0.8546
0.7219 0.79 1400 0.6221 0.8561
0.662 0.85 1500 0.6157 0.8574
0.662 0.91 1600 0.6138 0.8579
0.662 0.96 1700 0.6055 0.8595
0.662 1.02 1800 0.6143 0.8598
0.662 1.08 1900 0.6191 0.8599
0.5707 1.14 2000 0.6118 0.8607
0.5707 1.19 2100 0.6123 0.8611
0.5707 1.25 2200 0.6089 0.8617
0.5707 1.31 2300 0.6064 0.8619
0.5707 1.36 2400 0.6079 0.8625
0.4923 1.42 2500 0.6040 0.8625
0.4923 1.48 2600 0.6030 0.8630
0.4923 1.53 2700 0.6021 0.8636
0.4923 1.59 2800 0.6001 0.8643
0.4923 1.65 2900 0.5981 0.8644
0.4909 1.7 3000 0.5942 0.8648
0.4909 1.76 3100 0.5918 0.8650
0.4909 1.82 3200 0.5923 0.8659
0.4909 1.87 3300 0.5884 0.8664
0.4909 1.93 3400 0.5884 0.8663
0.4964 1.99 3500 0.5903 0.8669
0.4964 2.04 3600 0.6421 0.8655
0.4964 2.1 3700 0.6401 0.8651
0.4964 2.16 3800 0.6411 0.8649
0.4964 2.21 3900 0.6387 0.8645
0.345 2.27 4000 0.6362 0.8654
0.345 2.33 4100 0.6362 0.8654
0.345 2.38 4200 0.6362 0.8654
0.345 2.44 4300 0.6357 0.8655
0.345 2.5 4400 0.6362 0.8656
0.3463 2.55 4500 0.6377 0.8658
0.3463 2.61 4600 0.6357 0.8660
0.3463 2.67 4700 0.6294 0.8665
0.3463 2.72 4800 0.6333 0.8665
0.3463 2.78 4900 0.6362 0.8662
0.3508 2.84 5000 0.6357 0.8666
0.3508 2.89 5100 0.6299 0.8673
0.3508 2.95 5200 0.6313 0.8668
0.3508 3.01 5300 0.7188 0.8646
0.3508 3.06 5400 0.7017 0.8656
0.295 3.12 5500 0.6982 0.8653
0.295 3.18 5600 0.7031 0.8655
0.295 3.23 5700 0.6992 0.8651
0.295 3.29 5800 0.6997 0.8653
0.295 3.35 5900 0.7041 0.8651
0.2348 3.41 6000 0.7075 0.8649
0.2348 3.46 6100 0.6992 0.8650
0.2348 3.52 6200 0.7065 0.8647
0.2348 3.58 6300 0.6997 0.8652
0.2348 3.63 6400 0.7026 0.8651
0.2411 3.69 6500 0.7046 0.8656
0.2411 3.75 6600 0.7007 0.8655
0.2411 3.8 6700 0.7026 0.8651
0.2411 3.86 6800 0.7031 0.8655
0.2411 3.92 6900 0.7012 0.8658
0.251 3.97 7000 0.7051 0.8656
0.251 4.03 7100 0.7607 0.8650
0.251 4.09 7200 0.7632 0.8656
0.251 4.14 7300 0.7588 0.8655
0.251 4.2 7400 0.7578 0.8651
0.1797 4.26 7500 0.7710 0.8645
0.1797 4.31 7600 0.7627 0.8648
0.1797 4.37 7700 0.7583 0.8650
0.1797 4.43 7800 0.7646 0.8649
0.1797 4.48 7900 0.7598 0.8646
0.1784 4.54 8000 0.7656 0.8650
0.1784 4.6 8100 0.7617 0.8648
0.1784 4.65 8200 0.7573 0.8651
0.1784 4.71 8300 0.7671 0.8648
0.1784 4.77 8400 0.7563 0.8651
0.1827 4.82 8500 0.7651 0.8649
0.1827 4.88 8600 0.7637 0.8650
0.1827 4.94 8700 0.7607 0.8654
0.1827 4.99 8800 0.7607 0.8650
0.1827 5.05 8900 0.8149 0.8646
0.167 5.11 9000 0.8081 0.8648
0.167 5.16 9100 0.8184 0.8644
0.167 5.22 9200 0.8140 0.8647
0.167 5.28 9300 0.8169 0.8644
0.167 5.33 9400 0.8120 0.8645
0.1371 5.39 9500 0.8154 0.8643
0.1371 5.45 9600 0.8179 0.8642
0.1371 5.51 9700 0.8154 0.8643
0.1371 5.56 9800 0.8120 0.8645
0.1371 5.62 9900 0.8110 0.8650
0.1425 5.68 10000 0.8159 0.8645
0.1425 5.73 10100 0.8174 0.8646
0.1425 5.79 10200 0.8159 0.8649
0.1425 5.85 10300 0.8110 0.8639
0.1425 5.9 10400 0.8135 0.8645
0.1505 5.96 10500 0.8140 0.8642
0.1505 6.02 10600 0.8628 0.8640
0.1505 6.07 10700 0.8540 0.8644
0.1505 6.13 10800 0.8530 0.8642
0.1505 6.19 10900 0.8560 0.8647
0.1086 6.24 11000 0.8555 0.8649
0.1086 6.3 11100 0.8604 0.8644
0.1086 6.36 11200 0.8569 0.8642
0.1086 6.41 11300 0.8530 0.8639
0.1086 6.47 11400 0.8589 0.8643
0.1076 6.53 11500 0.8525 0.8639
0.1076 6.58 11600 0.8579 0.8640
0.1076 6.64 11700 0.8594 0.8640
0.1076 6.7 11800 0.8599 0.8643
0.1076 6.75 11900 0.8564 0.8640
0.1109 6.81 12000 0.8633 0.8640
0.1109 6.87 12100 0.8584 0.8638
0.1109 6.92 12200 0.8647 0.8636
0.1109 6.98 12300 0.8599 0.8635
0.1109 7.04 12400 0.8979 0.8632
0.1028 7.09 12500 0.8936 0.8635
0.1028 7.15 12600 0.9043 0.8637
0.1028 7.21 12700 0.8989 0.8642
0.1028 7.26 12800 0.8936 0.8642
0.1028 7.32 12900 0.8921 0.8641
0.0774 7.38 13000 0.8955 0.8634
0.0774 7.43 13100 0.8950 0.8636
0.0774 7.49 13200 0.8994 0.8635
0.0774 7.55 13300 0.8999 0.8635
0.0774 7.6 13400 0.8936 0.8631
0.0852 7.66 13500 0.9048 0.8634
0.0852 7.72 13600 0.8960 0.8632
0.0852 7.78 13700 0.9023 0.8635
0.0852 7.83 13800 0.8984 0.8638
0.0852 7.89 13900 0.9019 0.8635
0.0879 7.95 14000 0.9014 0.8634
0.0879 8.0 14100 0.9136 0.8630
0.0879 8.06 14200 0.9312 0.8639
0.0879 8.12 14300 0.9346 0.8635
0.0879 8.17 14400 0.9307 0.8635
0.0611 8.23 14500 0.9419 0.8641
0.0611 8.29 14600 0.9331 0.8631
0.0611 8.34 14700 0.9375 0.8636
0.0611 8.4 14800 0.9292 0.8626
0.0611 8.46 14900 0.9458 0.8637
0.061 8.51 15000 0.9336 0.8634
0.061 8.57 15100 0.9409 0.8630
0.061 8.63 15200 0.9390 0.8632
0.061 8.68 15300 0.9375 0.8628
0.061 8.74 15400 0.9365 0.8630
0.0646 8.8 15500 0.9370 0.8628
0.0646 8.85 15600 0.9355 0.8629
0.0646 8.91 15700 0.9375 0.8632
0.0646 8.97 15800 0.9390 0.8630
0.0646 9.02 15900 0.9717 0.8630
0.0593 9.08 16000 0.9673 0.8626
0.0593 9.14 16100 0.9644 0.8630
0.0593 9.19 16200 0.9624 0.8631
0.0593 9.25 16300 0.9648 0.8633
0.0593 9.31 16400 0.9673 0.8632
0.0415 9.36 16500 0.9658 0.8633
0.0415 9.42 16600 0.9688 0.8628
0.0415 9.48 16700 0.9653 0.8632
0.0415 9.53 16800 0.9658 0.8628
0.0415 9.59 16900 0.9668 0.8629
0.0471 9.65 17000 0.9604 0.8625
0.0471 9.7 17100 0.9658 0.8621
0.0471 9.76 17200 0.9731 0.8630
0.0471 9.82 17300 0.9692 0.8626
0.0471 9.88 17400 0.9673 0.8623
0.0528 9.93 17500 0.9614 0.8620
0.0528 9.99 17600 0.9697 0.8621

Framework versions

  • Transformers 4.21.0.dev0
  • Pytorch 1.12.1+cu113
  • Datasets 2.4.0
  • Tokenizers 0.12.1