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dfmodel_epoch3

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metadata
library_name: transformers
license: apache-2.0
base_model: Anshul15/dfmodel_epoch2
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: dfmodel_epoch3
    results: []

dfmodel_epoch3

This model is a fine-tuned version of Anshul15/dfmodel_epoch2 on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5686
  • Accuracy: 0.6282

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: 0.0001
  • train_batch_size: 1
  • eval_batch_size: 1
  • seed: 42
  • gradient_accumulation_steps: 32
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 1.0

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.0591 0.0126 10 0.0988 0.9854
0.1759 0.0252 20 0.2753 0.9298
0.387 0.0378 30 0.4861 0.6915
0.5898 0.0503 40 0.6165 0.5035
0.6176 0.0629 50 0.6575 0.6282
0.6772 0.0755 60 0.6412 0.4544
0.6112 0.0881 70 0.6178 0.6282
0.6387 0.1007 80 0.6395 0.6282
0.6325 0.1133 90 0.5668 0.6282
0.3027 0.1259 100 0.0538 0.9912
0.0551 0.1385 110 0.0631 0.9888
0.0382 0.1510 120 0.0700 0.9877
0.0683 0.1636 130 0.0695 0.9871
0.0869 0.1762 140 0.0642 0.9884
0.0341 0.1888 150 0.0602 0.9891
0.0688 0.2014 160 0.0598 0.9895
0.0391 0.2140 170 0.0604 0.9893
0.0216 0.2266 180 0.0605 0.9895
0.0675 0.2392 190 0.0600 0.9895
0.1167 0.2517 200 0.1437 0.9695
0.1678 0.2643 210 0.2156 0.9404
0.1978 0.2769 220 0.2365 0.9284
0.2246 0.2895 230 0.2520 0.9245
0.2508 0.3021 240 0.2419 0.9253
0.2886 0.3147 250 0.2349 0.9279
0.1919 0.3273 260 0.1301 0.9701
0.1344 0.3398 270 0.0668 0.9887
0.1049 0.3524 280 0.1200 0.9786
0.1485 0.3650 290 0.1284 0.9728
0.404 0.3776 300 0.7198 0.6366
0.6682 0.3902 310 0.6518 0.6282
0.6527 0.4028 320 0.6488 0.6282
0.675 0.4154 330 0.5839 0.6282
0.5118 0.4280 340 0.4812 0.6967
0.4476 0.4405 350 0.4491 0.7373
0.4241 0.4531 360 0.4362 0.7502
0.4772 0.4657 370 0.4350 0.7514
0.408 0.4783 380 0.4618 0.7511
0.5326 0.4909 390 0.5499 0.6282
0.5417 0.5035 400 0.5747 0.6282
0.5644 0.5161 410 0.5884 0.5368
0.5935 0.5287 420 0.5833 0.6282
0.611 0.5412 430 0.5841 0.6282
0.5653 0.5538 440 0.5833 0.6282
0.6195 0.5664 450 0.5838 0.6282
0.5475 0.5790 460 0.5894 0.6282
0.5987 0.5916 470 0.5847 0.6282
0.6024 0.6042 480 0.5911 0.5301
0.5649 0.6168 490 0.5865 0.6282
0.5802 0.6294 500 0.5889 0.6282
0.6224 0.6419 510 0.5955 0.5285
0.5903 0.6545 520 0.5844 0.6282
0.5752 0.6671 530 0.5864 0.6282
0.5967 0.6797 540 0.5861 0.6282
0.5692 0.6923 550 0.5936 0.6282
0.5897 0.7049 560 0.5991 0.6282
0.5739 0.7175 570 0.6019 0.6282
0.6136 0.7300 580 0.5948 0.6282
0.588 0.7426 590 0.5823 0.6282
0.6397 0.7552 600 0.5791 0.6282
0.5694 0.7678 610 0.5803 0.6282
0.574 0.7804 620 0.5781 0.6282
0.572 0.7930 630 0.5781 0.6282
0.5664 0.8056 640 0.5782 0.6282
0.5675 0.8182 650 0.5788 0.6282
0.5544 0.8307 660 0.5778 0.6282
0.5565 0.8433 670 0.5785 0.6282
0.5847 0.8559 680 0.5780 0.6282
0.5708 0.8685 690 0.5783 0.6282
0.5875 0.8811 700 0.5784 0.6282
0.6177 0.8937 710 0.5781 0.6282
0.5834 0.9063 720 0.5783 0.6282
0.5562 0.9189 730 0.5751 0.6282
0.5676 0.9314 740 0.5705 0.6282
0.5779 0.9440 750 0.5698 0.6282
0.5487 0.9566 760 0.5698 0.6282
0.572 0.9692 770 0.5690 0.6282
0.5687 0.9818 780 0.5687 0.6282
0.567 0.9944 790 0.5686 0.6282

Framework versions

  • Transformers 4.45.0.dev0
  • Pytorch 2.1.2
  • Datasets 2.21.1.dev0
  • Tokenizers 0.19.1