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Merged-MM-praj

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metadata
license: mit
base_model: prajjwal1/bert-tiny
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
model-index:
  - name: Merged-MM-praj
    results: []

Merged-MM-praj

This model is a fine-tuned version of prajjwal1/bert-tiny on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5525
  • Accuracy: 0.7777
  • F1: 0.8749

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: 3e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 7

Training results

Training Loss Epoch Step Validation Loss Accuracy F1
No log 0.0 50 0.6929 0.526 0.3813
No log 0.0 100 0.6938 0.48 0.3125
No log 0.01 150 0.6971 0.479 0.3103
No log 0.01 200 0.6948 0.479 0.3103
No log 0.01 250 0.6938 0.479 0.3103
No log 0.01 300 0.6939 0.479 0.3103
No log 0.01 350 0.6927 0.521 0.3587
No log 0.02 400 0.6931 0.501 0.4988
No log 0.02 450 0.6944 0.479 0.3103
0.6942 0.02 500 0.6954 0.479 0.3103
0.6942 0.02 550 0.6960 0.479 0.3103
0.6942 0.02 600 0.6934 0.486 0.3322
0.6942 0.02 650 0.6970 0.479 0.3103
0.6942 0.03 700 0.6929 0.535 0.4767
0.6942 0.03 750 0.6931 0.499 0.4609
0.6942 0.03 800 0.6952 0.479 0.3103
0.6942 0.03 850 0.6933 0.48 0.3160
0.6942 0.03 900 0.6979 0.479 0.3103
0.6942 0.04 950 0.6940 0.479 0.3103
0.6938 0.04 1000 0.6915 0.521 0.3569
0.6938 0.04 1050 0.6942 0.479 0.3103
0.6938 0.04 1100 0.6884 0.519 0.3630
0.6938 0.04 1150 0.6849 0.596 0.5817
0.6938 0.05 1200 0.6849 0.547 0.5131
0.6938 0.05 1250 0.6771 0.568 0.5502
0.6938 0.05 1300 0.6792 0.572 0.5558
0.6938 0.05 1350 0.6889 0.55 0.5161
0.6938 0.05 1400 0.6792 0.59 0.5828
0.6938 0.06 1450 0.6729 0.602 0.5987
0.6781 0.06 1500 0.6702 0.592 0.5822
0.6781 0.06 1550 0.6711 0.578 0.5633
0.6781 0.06 1600 0.6642 0.607 0.6024
0.6781 0.06 1650 0.6624 0.592 0.5819
0.6781 0.07 1700 0.6585 0.595 0.5883
0.6781 0.07 1750 0.6543 0.584 0.5740
0.6781 0.07 1800 0.6452 0.6 0.5926
0.6781 0.07 1850 0.6355 0.615 0.6106
0.6781 0.07 1900 0.6280 0.615 0.6090
0.6781 0.07 1950 0.6209 0.621 0.6139
0.6465 0.08 2000 0.6178 0.632 0.6247
0.6465 0.08 2050 0.6133 0.641 0.6303
0.6465 0.08 2100 0.6132 0.629 0.6218
0.6465 0.08 2150 0.6155 0.63 0.6289
0.6465 0.08 2200 0.5984 0.635 0.6322
0.6465 0.09 2250 0.6065 0.633 0.6102
0.6465 0.09 2300 0.5968 0.629 0.6063
0.6465 0.09 2350 0.5871 0.649 0.6411
0.6465 0.09 2400 0.5824 0.64 0.6218
0.6465 0.09 2450 0.5812 0.643 0.6390
0.6042 0.1 2500 0.5790 0.644 0.6355
0.6042 0.1 2550 0.5744 0.654 0.6507
0.6042 0.1 2600 0.5679 0.641 0.6292
0.6042 0.1 2650 0.5707 0.644 0.6311
0.6042 0.1 2700 0.5707 0.652 0.6439
0.6042 0.11 2750 0.5680 0.661 0.6569
0.6042 0.11 2800 0.5592 0.67 0.6684
0.6042 0.11 2850 0.5557 0.678 0.6758
0.6042 0.11 2900 0.5579 0.671 0.6690
0.6042 0.11 2950 0.5490 0.692 0.6909
0.5834 0.11 3000 0.5474 0.688 0.6858
0.5834 0.12 3050 0.5447 0.696 0.6902
0.5834 0.12 3100 0.5456 0.699 0.6985
0.5834 0.12 3150 0.5592 0.675 0.6628
0.5834 0.12 3200 0.5442 0.69 0.6856
0.5834 0.12 3250 0.5424 0.698 0.6974
0.5834 0.13 3300 0.5464 0.691 0.6907
0.5834 0.13 3350 0.5433 0.693 0.6922
0.5834 0.13 3400 0.5400 0.746 0.7461
0.5834 0.13 3450 0.5406 0.712 0.7091
0.5551 0.13 3500 0.5367 0.738 0.7376
0.5551 0.14 3550 0.5354 0.713 0.7091
0.5551 0.14 3600 0.5377 0.74 0.7400
0.5551 0.14 3650 0.5342 0.751 0.7506
0.5551 0.14 3700 0.5386 0.701 0.6992
0.5551 0.14 3750 0.5395 0.737 0.7368
0.5551 0.15 3800 0.5333 0.733 0.7330
0.5551 0.15 3850 0.5245 0.737 0.7371
0.5551 0.15 3900 0.5236 0.745 0.7451
0.5551 0.15 3950 0.5149 0.741 0.7400
0.5508 0.15 4000 0.5208 0.743 0.7422
0.5508 0.16 4050 0.5109 0.744 0.7440
0.5508 0.16 4100 0.5179 0.742 0.7398
0.5508 0.16 4150 0.5133 0.75 0.7499
0.5508 0.16 4200 0.5110 0.744 0.7416
0.5508 0.16 4250 0.5133 0.749 0.7476
0.5508 0.16 4300 0.5075 0.743 0.7410
0.5508 0.17 4350 0.5108 0.755 0.7544
0.5508 0.17 4400 0.5051 0.747 0.7465
0.5508 0.17 4450 0.5064 0.746 0.7455
0.5362 0.17 4500 0.5030 0.744 0.7441
0.5362 0.17 4550 0.5043 0.748 0.7476
0.5362 0.18 4600 0.5010 0.753 0.7531
0.5362 0.18 4650 0.4988 0.762 0.7616
0.5362 0.18 4700 0.4999 0.755 0.7548
0.5362 0.18 4750 0.5159 0.754 0.7529
0.5362 0.18 4800 0.4924 0.764 0.7639
0.5362 0.19 4850 0.4935 0.755 0.7549
0.5362 0.19 4900 0.4874 0.76 0.7601
0.5362 0.19 4950 0.4859 0.759 0.7591
0.5226 0.19 5000 0.4901 0.761 0.7610
0.5226 0.19 5050 0.4740 0.779 0.7790
0.5226 0.2 5100 0.4799 0.783 0.7831
0.5226 0.2 5150 0.4833 0.771 0.7698
0.5226 0.2 5200 0.4879 0.759 0.7561
0.5226 0.2 5250 0.4812 0.772 0.7719
0.5226 0.2 5300 0.4825 0.772 0.7715
0.5226 0.2 5350 0.4791 0.775 0.7744
0.5226 0.21 5400 0.4749 0.773 0.7729
0.5226 0.21 5450 0.4691 0.782 0.7811
0.5055 0.21 5500 0.4752 0.78 0.7791
0.5055 0.21 5550 0.4621 0.766 0.7645
0.5055 0.21 5600 0.4628 0.779 0.7790
0.5055 0.22 5650 0.4543 0.776 0.7760
0.5055 0.22 5700 0.4548 0.786 0.7861
0.5055 0.22 5750 0.4578 0.777 0.7763
0.5055 0.22 5800 0.4684 0.778 0.7780
0.5055 0.22 5850 0.4626 0.775 0.7751
0.5055 0.23 5900 0.4714 0.785 0.7850
0.5055 0.23 5950 0.4514 0.79 0.7896
0.4985 0.23 6000 0.4541 0.773 0.7731
0.4985 0.23 6050 0.4587 0.788 0.7876
0.4985 0.23 6100 0.4523 0.787 0.7867
0.4985 0.24 6150 0.4441 0.787 0.7870
0.4985 0.24 6200 0.4529 0.784 0.7841
0.4985 0.24 6250 0.4512 0.784 0.7840
0.4985 0.24 6300 0.4545 0.777 0.7757
0.4985 0.24 6350 0.4399 0.788 0.7874
0.4985 0.25 6400 0.4478 0.794 0.7939
0.4985 0.25 6450 0.4495 0.793 0.7930
0.4937 0.25 6500 0.4454 0.792 0.7913
0.4937 0.25 6550 0.4438 0.795 0.7950
0.4937 0.25 6600 0.4476 0.795 0.7948
0.4937 0.25 6650 0.4448 0.794 0.7939
0.4937 0.26 6700 0.4472 0.791 0.7911
0.4937 0.26 6750 0.4431 0.793 0.7924
0.4937 0.26 6800 0.4434 0.796 0.7958
0.4937 0.26 6850 0.4340 0.802 0.802
0.4937 0.26 6900 0.4502 0.786 0.7848
0.4937 0.27 6950 0.4349 0.797 0.7964
0.4826 0.27 7000 0.4348 0.79 0.7894
0.4826 0.27 7050 0.4321 0.788 0.7875
0.4826 0.27 7100 0.4300 0.787 0.7868
0.4826 0.27 7150 0.4346 0.78 0.7779
0.4826 0.28 7200 0.4246 0.802 0.8020
0.4826 0.28 7250 0.4273 0.793 0.7930
0.4826 0.28 7300 0.4346 0.79 0.7894
0.4826 0.28 7350 0.4358 0.789 0.7887
0.4826 0.28 7400 0.4368 0.788 0.7871
0.4826 0.29 7450 0.4426 0.784 0.7841
0.4756 0.29 7500 0.4312 0.802 0.8019
0.4756 0.29 7550 0.4303 0.795 0.7944
0.4756 0.29 7600 0.4391 0.792 0.7916
0.4756 0.29 7650 0.4325 0.793 0.7922
0.4756 0.29 7700 0.4283 0.793 0.7920
0.4756 0.3 7750 0.4271 0.799 0.7991

Framework versions

  • Transformers 4.35.2
  • Pytorch 2.1.0+cu121
  • Datasets 2.16.0
  • Tokenizers 0.15.0