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mlcovid19-classifier

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metadata
license: mit
tags:
  - generated_from_trainer
model-index:
  - name: mlcovid19-classifier
    results: []

mlcovid19-classifier

This model is a fine-tuned version of oscarwu/mlcovid19-classifier on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2879
  • F1 Macro: 0.7978
  • F1 Misinformation: 0.9347
  • F1 Factual: 0.9423
  • F1 Other: 0.5166
  • Prec Macro: 0.8156
  • Prec Misinformation: 0.9277
  • Prec Factual: 0.9345
  • Prec Other: 0.5846

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: 2e-05
  • train_batch_size: 128
  • eval_batch_size: 128
  • seed: 42
  • gradient_accumulation_steps: 32
  • total_train_batch_size: 4096
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 2607
  • num_epochs: 400

Training results

Training Loss Epoch Step Validation Loss F1 Macro F1 Misinformation F1 Factual F1 Other Prec Macro Prec Misinformation Prec Factual Prec Other
0.4535 1.98 10 0.4122 0.6809 0.8906 0.8993 0.2529 0.7749 0.8433 0.9169 0.5646
0.4445 3.98 20 0.4056 0.6844 0.8918 0.9004 0.2611 0.7706 0.8461 0.9171 0.5487
0.4362 5.98 30 0.3966 0.6870 0.8930 0.9020 0.2658 0.7672 0.8490 0.9171 0.5356
0.4229 7.98 40 0.3864 0.6885 0.8955 0.9055 0.2645 0.7652 0.8531 0.9179 0.5246
0.4134 9.98 50 0.3774 0.6889 0.8983 0.9091 0.2594 0.7697 0.8573 0.9173 0.5345
0.4004 11.98 60 0.3682 0.6907 0.8996 0.9111 0.2616 0.7763 0.8605 0.9148 0.5536
0.3893 13.98 70 0.3583 0.6960 0.9014 0.9124 0.2740 0.7853 0.8629 0.9152 0.5778
0.3853 15.98 80 0.3483 0.7036 0.9031 0.9157 0.2920 0.7749 0.8683 0.9172 0.5390
0.369 17.98 90 0.3399 0.7011 0.9037 0.9167 0.2828 0.7775 0.8690 0.9159 0.5476
0.36 19.98 100 0.3312 0.7102 0.9056 0.9194 0.3055 0.7836 0.8733 0.9167 0.5609
0.3445 21.98 110 0.3237 0.7116 0.9065 0.9204 0.3078 0.7860 0.8749 0.9165 0.5667
0.3406 23.98 120 0.3181 0.7058 0.9068 0.9212 0.2893 0.7880 0.8740 0.9162 0.5738
0.3286 25.98 130 0.3094 0.7183 0.9099 0.9250 0.32 0.7932 0.8782 0.9216 0.5797
0.3213 27.98 140 0.3049 0.7187 0.9111 0.9254 0.3196 0.7957 0.8800 0.9204 0.5867
0.3111 29.98 150 0.3017 0.7219 0.9129 0.9264 0.3263 0.7983 0.8843 0.9178 0.5927
0.3087 31.98 160 0.2970 0.7231 0.9132 0.9276 0.3287 0.7977 0.8850 0.9188 0.5893
0.2992 33.98 170 0.2926 0.7243 0.9141 0.9293 0.3293 0.8003 0.8839 0.9235 0.5935
0.2924 35.98 180 0.2892 0.7312 0.9150 0.9303 0.3482 0.7971 0.8889 0.9218 0.5806
0.2878 37.98 190 0.2870 0.7356 0.9173 0.9324 0.3571 0.8027 0.8906 0.9246 0.5929
0.2811 39.98 200 0.2844 0.7439 0.9188 0.9328 0.3801 0.8109 0.8954 0.9213 0.6161
0.2751 41.98 210 0.2816 0.7500 0.9197 0.9340 0.3963 0.8060 0.8973 0.9250 0.5956
0.2683 43.98 220 0.2798 0.7517 0.9210 0.9339 0.4000 0.8068 0.8976 0.9272 0.5956
0.2643 45.98 230 0.2766 0.7544 0.9221 0.9349 0.4062 0.8064 0.8990 0.9290 0.5910
0.2619 47.98 240 0.2736 0.7579 0.9227 0.9356 0.4155 0.8085 0.9002 0.9298 0.5954
0.2539 49.98 250 0.2733 0.7567 0.9231 0.9357 0.4111 0.8060 0.9006 0.9302 0.5872
0.2496 51.98 260 0.2713 0.7600 0.9235 0.9360 0.4206 0.8070 0.9009 0.9320 0.5881
0.2455 53.98 270 0.2697 0.7575 0.9231 0.9356 0.4139 0.8052 0.9009 0.9304 0.5844
0.2371 55.98 280 0.2686 0.7652 0.9239 0.9356 0.4360 0.8058 0.9058 0.9283 0.5833
0.2316 57.98 290 0.2686 0.7664 0.9243 0.9361 0.4389 0.8037 0.9073 0.9288 0.5749
0.2258 59.98 300 0.2664 0.7680 0.9247 0.9360 0.4431 0.8018 0.9095 0.9279 0.5680
0.2207 61.98 310 0.2663 0.7736 0.9262 0.9373 0.4574 0.8015 0.9145 0.9276 0.5625
0.2167 63.98 320 0.2643 0.7715 0.9268 0.9380 0.4498 0.8003 0.9127 0.9312 0.5571
0.2131 65.98 330 0.2627 0.7753 0.9287 0.9398 0.4573 0.8064 0.9123 0.9356 0.5714
0.2075 67.98 340 0.2644 0.7760 0.9290 0.9397 0.4593 0.8056 0.9136 0.9349 0.5682
0.2049 69.98 350 0.2648 0.7768 0.9290 0.9390 0.4623 0.8050 0.9174 0.9292 0.5685
0.2016 71.98 360 0.2631 0.7771 0.9295 0.9394 0.4623 0.8055 0.9165 0.9316 0.5685
0.1979 73.98 370 0.2644 0.7793 0.9305 0.9397 0.4677 0.8041 0.9208 0.9295 0.5620
0.1939 75.98 380 0.2671 0.7909 0.9312 0.9392 0.5023 0.8099 0.9272 0.9256 0.5771
0.1932 77.98 390 0.2648 0.7927 0.9325 0.9422 0.5035 0.8104 0.9242 0.9361 0.5709
0.1856 79.98 400 0.2615 0.7922 0.9331 0.9431 0.5004 0.8111 0.9235 0.9379 0.5719
0.1837 81.98 410 0.2624 0.7898 0.9328 0.9447 0.4920 0.8141 0.9183 0.9432 0.5808
0.1781 83.98 420 0.2660 0.7988 0.9334 0.9432 0.5196 0.8128 0.9263 0.9388 0.5733
0.172 85.98 430 0.2642 0.7909 0.9335 0.9428 0.4964 0.8139 0.9234 0.9353 0.5829
0.172 87.98 440 0.2695 0.7880 0.9321 0.9430 0.4889 0.8121 0.9172 0.9422 0.5771
0.1656 89.98 450 0.2671 0.7928 0.9337 0.9436 0.5012 0.8145 0.9212 0.9411 0.5811
0.163 91.98 460 0.2693 0.7949 0.9331 0.9429 0.5088 0.8111 0.9232 0.9408 0.5692
0.1555 93.98 470 0.2696 0.7967 0.9332 0.9431 0.5138 0.8142 0.9203 0.9449 0.5776
0.1513 95.98 480 0.2710 0.7985 0.9340 0.9443 0.5172 0.8156 0.9220 0.9450 0.5798
0.1478 97.98 490 0.2722 0.7991 0.9342 0.9442 0.5189 0.8138 0.9243 0.9436 0.5736
0.1435 99.98 500 0.2725 0.7981 0.9343 0.9432 0.5166 0.8124 0.9248 0.9424 0.57
0.1409 101.98 510 0.2754 0.7994 0.9345 0.9432 0.5206 0.8161 0.9231 0.9433 0.5819
0.1384 103.98 520 0.2817 0.7991 0.9347 0.9441 0.5184 0.8166 0.9233 0.9436 0.5828
0.1333 105.98 530 0.2833 0.7934 0.9351 0.9434 0.5016 0.8178 0.9232 0.9380 0.5921
0.1267 107.98 540 0.2929 0.7884 0.9337 0.9429 0.4886 0.8167 0.9198 0.9377 0.5925
0.1234 109.98 550 0.2879 0.7978 0.9347 0.9423 0.5166 0.8156 0.9277 0.9345 0.5846

Framework versions

  • Transformers 4.23.1
  • Pytorch 1.12.1+cu113
  • Datasets 2.6.1
  • Tokenizers 0.13.1