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RobertaTuned

Text Classification
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/kaggle/working/TTC4900Model

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  1. README.md +140 -0
  2. config.json +46 -0
  3. model.safetensors +3 -0
  4. training_args.bin +3 -0
README.md ADDED
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+ ---
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+ license: mit
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+ base_model: roberta-base
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ - f1
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+ - precision
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+ - recall
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+ model-index:
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+ - name: TTC4900Model
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # TTC4900Model
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+
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+ This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.5463
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+ - Accuracy: 0.8381
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+ - F1: 0.7260
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+ - Precision: 0.7596
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+ - Recall: 0.7047
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 32
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+ - eval_batch_size: 64
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_steps: 100
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+ - num_epochs: 3
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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+ | 1.4742 | 0.04 | 50 | 1.3501 | 0.5138 | 0.1534 | 0.1802 | 0.2019 |
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+ | 1.3854 | 0.08 | 100 | 1.2962 | 0.5492 | 0.1444 | 0.1464 | 0.1712 |
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+ | 1.2044 | 0.12 | 150 | 1.1191 | 0.6135 | 0.1935 | 0.2802 | 0.2438 |
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+ | 1.1934 | 0.16 | 200 | 1.1296 | 0.6328 | 0.2580 | 0.3115 | 0.2543 |
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+ | 1.0589 | 0.2 | 250 | 0.9550 | 0.6909 | 0.3103 | 0.4154 | 0.3417 |
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+ | 1.0216 | 0.24 | 300 | 0.9304 | 0.7036 | 0.3720 | 0.3727 | 0.3799 |
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+ | 0.9372 | 0.28 | 350 | 0.8312 | 0.7185 | 0.4050 | 0.7342 | 0.3833 |
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+ | 0.8818 | 0.33 | 400 | 0.8910 | 0.7197 | 0.4773 | 0.5010 | 0.4834 |
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+ | 0.8957 | 0.37 | 450 | 0.7688 | 0.7512 | 0.4872 | 0.5636 | 0.4631 |
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+ | 0.8311 | 0.41 | 500 | 0.7380 | 0.7638 | 0.5687 | 0.6187 | 0.5541 |
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+ | 0.7595 | 0.45 | 550 | 0.7932 | 0.7435 | 0.5502 | 0.5931 | 0.5648 |
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+ | 0.7677 | 0.49 | 600 | 0.7167 | 0.7746 | 0.5999 | 0.6017 | 0.6063 |
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+ | 0.7386 | 0.53 | 650 | 0.6960 | 0.7776 | 0.5608 | 0.6716 | 0.5294 |
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+ | 0.7731 | 0.57 | 700 | 0.6524 | 0.7973 | 0.6137 | 0.6762 | 0.5856 |
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+ | 0.6949 | 0.61 | 750 | 0.6898 | 0.7880 | 0.6079 | 0.6804 | 0.5660 |
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+ | 0.6982 | 0.65 | 800 | 0.6676 | 0.7882 | 0.6021 | 0.6450 | 0.5858 |
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+ | 0.6805 | 0.69 | 850 | 0.6533 | 0.7989 | 0.6216 | 0.7456 | 0.6235 |
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+ | 0.7633 | 0.73 | 900 | 0.7205 | 0.7796 | 0.5835 | 0.6257 | 0.6041 |
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+ | 0.7712 | 0.77 | 950 | 0.7247 | 0.7838 | 0.5740 | 0.6818 | 0.5463 |
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+ | 0.6768 | 0.81 | 1000 | 0.6328 | 0.8051 | 0.6448 | 0.7470 | 0.6220 |
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+ | 0.671 | 0.85 | 1050 | 0.7261 | 0.7767 | 0.5529 | 0.6892 | 0.5497 |
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+ | 0.6413 | 0.89 | 1100 | 0.6102 | 0.8100 | 0.6359 | 0.6886 | 0.6147 |
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+ | 0.6398 | 0.93 | 1150 | 0.6881 | 0.7857 | 0.5860 | 0.8209 | 0.5796 |
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+ | 0.6588 | 0.98 | 1200 | 0.6264 | 0.8056 | 0.6416 | 0.6564 | 0.6405 |
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+ | 0.5952 | 1.02 | 1250 | 0.6763 | 0.8119 | 0.6407 | 0.6848 | 0.6231 |
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+ | 0.5342 | 1.06 | 1300 | 0.7901 | 0.7930 | 0.5880 | 0.6963 | 0.5642 |
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+ | 0.5187 | 1.1 | 1350 | 0.6499 | 0.8073 | 0.6686 | 0.7048 | 0.6669 |
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+ | 0.5655 | 1.14 | 1400 | 0.6369 | 0.8061 | 0.6759 | 0.6753 | 0.6796 |
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+ | 0.5522 | 1.18 | 1450 | 0.6168 | 0.8089 | 0.6496 | 0.6933 | 0.6619 |
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+ | 0.5308 | 1.22 | 1500 | 0.6293 | 0.8173 | 0.6627 | 0.7965 | 0.6479 |
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+ | 0.628 | 1.26 | 1550 | 0.6275 | 0.8086 | 0.6672 | 0.7533 | 0.6413 |
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+ | 0.4993 | 1.3 | 1600 | 0.6286 | 0.8150 | 0.6753 | 0.7726 | 0.6521 |
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+ | 0.5557 | 1.34 | 1650 | 0.6392 | 0.8145 | 0.6380 | 0.7942 | 0.6101 |
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+ | 0.5315 | 1.38 | 1700 | 0.6072 | 0.8222 | 0.6863 | 0.7386 | 0.6572 |
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+ | 0.5766 | 1.42 | 1750 | 0.6300 | 0.8120 | 0.6318 | 0.8268 | 0.6121 |
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+ | 0.5225 | 1.46 | 1800 | 0.5962 | 0.8195 | 0.6903 | 0.7529 | 0.6648 |
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+ | 0.5074 | 1.5 | 1850 | 0.6217 | 0.8196 | 0.6622 | 0.7711 | 0.6262 |
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+ | 0.5613 | 1.54 | 1900 | 0.5924 | 0.8246 | 0.7053 | 0.7634 | 0.6756 |
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+ | 0.5097 | 1.59 | 1950 | 0.5728 | 0.8233 | 0.6791 | 0.7823 | 0.6391 |
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+ | 0.5001 | 1.63 | 2000 | 0.5828 | 0.8300 | 0.7151 | 0.7483 | 0.6918 |
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+ | 0.5144 | 1.67 | 2050 | 0.5746 | 0.8256 | 0.6997 | 0.7606 | 0.6727 |
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+ | 0.5462 | 1.71 | 2100 | 0.5792 | 0.8229 | 0.6932 | 0.7236 | 0.6943 |
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+ | 0.5252 | 1.75 | 2150 | 0.5827 | 0.8266 | 0.6926 | 0.7896 | 0.6572 |
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+ | 0.5369 | 1.79 | 2200 | 0.6034 | 0.8142 | 0.6867 | 0.7556 | 0.6558 |
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+ | 0.5144 | 1.83 | 2250 | 0.5748 | 0.8280 | 0.7103 | 0.7445 | 0.6937 |
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+ | 0.545 | 1.87 | 2300 | 0.5671 | 0.8243 | 0.6942 | 0.7573 | 0.6910 |
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+ | 0.5151 | 1.91 | 2350 | 0.5685 | 0.8292 | 0.6961 | 0.7770 | 0.6678 |
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+ | 0.5268 | 1.95 | 2400 | 0.5470 | 0.8318 | 0.7171 | 0.7650 | 0.6974 |
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+ | 0.509 | 1.99 | 2450 | 0.5448 | 0.8336 | 0.7126 | 0.7736 | 0.6885 |
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+ | 0.4062 | 2.03 | 2500 | 0.6064 | 0.8329 | 0.6949 | 0.7580 | 0.6716 |
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+ | 0.452 | 2.07 | 2550 | 0.5852 | 0.8291 | 0.7058 | 0.7678 | 0.6852 |
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+ | 0.488 | 2.11 | 2600 | 0.5741 | 0.8283 | 0.6993 | 0.7521 | 0.6897 |
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+ | 0.4459 | 2.15 | 2650 | 0.5606 | 0.8319 | 0.7094 | 0.7706 | 0.6829 |
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+ | 0.4588 | 2.2 | 2700 | 0.5834 | 0.8253 | 0.7106 | 0.7520 | 0.6914 |
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+ | 0.4325 | 2.24 | 2750 | 0.5672 | 0.8299 | 0.7149 | 0.7590 | 0.6895 |
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+ | 0.4182 | 2.28 | 2800 | 0.5661 | 0.8316 | 0.7190 | 0.7527 | 0.7071 |
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+ | 0.4524 | 2.32 | 2850 | 0.5719 | 0.8329 | 0.7176 | 0.7715 | 0.6936 |
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+ | 0.4078 | 2.36 | 2900 | 0.5574 | 0.8308 | 0.7149 | 0.7479 | 0.7035 |
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+ | 0.3654 | 2.4 | 2950 | 0.5658 | 0.8353 | 0.7188 | 0.7521 | 0.7002 |
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+ | 0.4095 | 2.44 | 3000 | 0.5608 | 0.8335 | 0.7213 | 0.7524 | 0.7019 |
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+ | 0.379 | 2.48 | 3050 | 0.5666 | 0.8365 | 0.7211 | 0.7739 | 0.6949 |
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+ | 0.3939 | 2.52 | 3100 | 0.5711 | 0.8296 | 0.7203 | 0.7621 | 0.6954 |
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+ | 0.4039 | 2.56 | 3150 | 0.5748 | 0.8341 | 0.7213 | 0.7641 | 0.6942 |
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+ | 0.4034 | 2.6 | 3200 | 0.5533 | 0.8348 | 0.7282 | 0.7593 | 0.7065 |
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+ | 0.4412 | 2.64 | 3250 | 0.5490 | 0.8357 | 0.7250 | 0.7805 | 0.6944 |
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+ | 0.386 | 2.68 | 3300 | 0.5675 | 0.8353 | 0.7296 | 0.7605 | 0.7093 |
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+ | 0.4298 | 2.72 | 3350 | 0.5525 | 0.8344 | 0.7320 | 0.7583 | 0.7140 |
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+ | 0.384 | 2.76 | 3400 | 0.5629 | 0.8355 | 0.7240 | 0.7734 | 0.7004 |
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+ | 0.3909 | 2.8 | 3450 | 0.5586 | 0.8344 | 0.7269 | 0.7562 | 0.7132 |
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+ | 0.3975 | 2.85 | 3500 | 0.5538 | 0.8356 | 0.7253 | 0.7679 | 0.7022 |
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+ | 0.3906 | 2.89 | 3550 | 0.5566 | 0.8332 | 0.7246 | 0.7570 | 0.7091 |
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+ | 0.3707 | 2.93 | 3600 | 0.5575 | 0.8359 | 0.7290 | 0.7619 | 0.7095 |
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+ | 0.3995 | 2.97 | 3650 | 0.5529 | 0.8345 | 0.7296 | 0.7563 | 0.7131 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.38.2
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+ - Pytorch 2.1.2
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+ - Datasets 2.1.0
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+ - Tokenizers 0.15.2
config.json ADDED
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+ {
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+ "_name_or_path": "roberta-base",
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+ "architectures": [
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+ "BertForSequenceClassification"
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+ ],
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+ "attention_probs_dropout_prob": 0.1,
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+ "bos_token_id": 0,
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+ "hidden_act": "gelu",
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+ "hidden_size": 768,
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+ "id2label": {
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+ "0": "Skill",
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+ "1": "Exp",
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+ "2": "PI",
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+ "3": "Sum",
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+ "4": "Edu",
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+ "5": "Obj",
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+ "6": "QC"
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+ "label2id": {
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+ "Exp": 1,
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+ "Obj": 5,
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+ "PI": 2,
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+ "QC": 6,
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+ "Skill": 0,
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+ "Sum": 3
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+ },
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+ "layer_norm_eps": 1e-05,
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+ "max_position_embeddings": 514,
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+ "model_type": "bert",
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+ "num_attention_heads": 12,
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+ "num_hidden_layers": 12,
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+ "pad_token_id": 1,
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+ "position_embedding_type": "absolute",
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+ "problem_type": "single_label_classification",
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.38.2",
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+ "type_vocab_size": 1,
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+ "use_cache": true,
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+ "vocab_size": 50265
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+ }
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