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End of training

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  1. README.md +53 -53
  2. adapter_model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -17,9 +17,9 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_27M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_27M) on the [mahdibaghbanzadeh/GUE_EMP_H4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H4) dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2596
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- - F1 Score: 0.8990
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- - Accuracy: 0.8994
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  ## Model description
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@@ -50,56 +50,56 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
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  |:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|
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- | 0.3344 | 2.17 | 200 | 0.2833 | 0.8947 | 0.8946 |
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- | 0.2613 | 4.35 | 400 | 0.2697 | 0.8952 | 0.8953 |
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- | 0.2448 | 6.52 | 600 | 0.2689 | 0.9007 | 0.9008 |
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- | 0.2336 | 8.7 | 800 | 0.2780 | 0.8913 | 0.8912 |
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- | 0.2122 | 10.87 | 1000 | 0.2770 | 0.8940 | 0.8939 |
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- | 0.205 | 13.04 | 1200 | 0.2818 | 0.8968 | 0.8966 |
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- | 0.186 | 15.22 | 1400 | 0.2895 | 0.8941 | 0.8939 |
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- | 0.1726 | 17.39 | 1600 | 0.3137 | 0.8874 | 0.8871 |
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- | 0.1593 | 19.57 | 1800 | 0.3108 | 0.8898 | 0.8898 |
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- | 0.1454 | 21.74 | 2000 | 0.3295 | 0.8798 | 0.8795 |
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- | 0.1317 | 23.91 | 2200 | 0.3456 | 0.8848 | 0.8850 |
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- | 0.1247 | 26.09 | 2400 | 0.3373 | 0.8849 | 0.8850 |
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- | 0.1073 | 28.26 | 2600 | 0.3978 | 0.8842 | 0.8843 |
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- | 0.0975 | 30.43 | 2800 | 0.4058 | 0.8789 | 0.8789 |
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- | 0.0828 | 32.61 | 3000 | 0.4454 | 0.8718 | 0.8720 |
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- | 0.0786 | 34.78 | 3200 | 0.4245 | 0.8897 | 0.8898 |
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- | 0.0722 | 36.96 | 3400 | 0.4648 | 0.8799 | 0.8802 |
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- | 0.0607 | 39.13 | 3600 | 0.5033 | 0.8738 | 0.8741 |
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- | 0.0591 | 41.3 | 3800 | 0.4646 | 0.8830 | 0.8830 |
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- | 0.053 | 43.48 | 4000 | 0.5155 | 0.8723 | 0.8720 |
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- | 0.048 | 45.65 | 4200 | 0.5738 | 0.8689 | 0.8693 |
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- | 0.0458 | 47.83 | 4400 | 0.5701 | 0.8768 | 0.8768 |
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- | 0.042 | 50.0 | 4600 | 0.5922 | 0.8682 | 0.8686 |
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- | 0.039 | 52.17 | 4800 | 0.6313 | 0.8734 | 0.8734 |
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- | 0.0365 | 54.35 | 5000 | 0.6028 | 0.8801 | 0.8802 |
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- | 0.0328 | 56.52 | 5200 | 0.6634 | 0.8709 | 0.8706 |
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- | 0.0332 | 58.7 | 5400 | 0.6220 | 0.8747 | 0.8747 |
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- | 0.0279 | 60.87 | 5600 | 0.6763 | 0.8703 | 0.8700 |
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- | 0.0316 | 63.04 | 5800 | 0.6680 | 0.8689 | 0.8686 |
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- | 0.0272 | 65.22 | 6000 | 0.6361 | 0.8774 | 0.8775 |
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- | 0.0237 | 67.39 | 6200 | 0.6719 | 0.8734 | 0.8734 |
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- | 0.0284 | 69.57 | 6400 | 0.6502 | 0.8774 | 0.8775 |
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- | 0.0238 | 71.74 | 6600 | 0.7002 | 0.8786 | 0.8789 |
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- | 0.0219 | 73.91 | 6800 | 0.6923 | 0.8781 | 0.8782 |
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- | 0.0184 | 76.09 | 7000 | 0.7053 | 0.8795 | 0.8795 |
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- | 0.0192 | 78.26 | 7200 | 0.7043 | 0.8857 | 0.8857 |
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- | 0.0204 | 80.43 | 7400 | 0.7248 | 0.8830 | 0.8830 |
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- | 0.0202 | 82.61 | 7600 | 0.7226 | 0.8764 | 0.8768 |
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- | 0.0199 | 84.78 | 7800 | 0.7160 | 0.8884 | 0.8884 |
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- | 0.016 | 86.96 | 8000 | 0.7167 | 0.8822 | 0.8823 |
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- | 0.0167 | 89.13 | 8200 | 0.7441 | 0.8788 | 0.8789 |
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- | 0.0153 | 91.3 | 8400 | 0.7368 | 0.8781 | 0.8782 |
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- | 0.0139 | 93.48 | 8600 | 0.7587 | 0.8808 | 0.8809 |
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- | 0.0138 | 95.65 | 8800 | 0.7746 | 0.8761 | 0.8761 |
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- | 0.0144 | 97.83 | 9000 | 0.7587 | 0.8836 | 0.8836 |
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- | 0.0139 | 100.0 | 9200 | 0.7791 | 0.8823 | 0.8823 |
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- | 0.015 | 102.17 | 9400 | 0.7806 | 0.8809 | 0.8809 |
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- | 0.0126 | 104.35 | 9600 | 0.7763 | 0.8795 | 0.8795 |
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- | 0.0115 | 106.52 | 9800 | 0.7799 | 0.8808 | 0.8809 |
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- | 0.0142 | 108.7 | 10000 | 0.7773 | 0.8788 | 0.8789 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_27M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_27M) on the [mahdibaghbanzadeh/GUE_EMP_H4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H4) dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2609
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+ - F1 Score: 0.8964
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+ - Accuracy: 0.8966
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
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  |:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|
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+ | 0.3346 | 2.17 | 200 | 0.2842 | 0.8954 | 0.8953 |
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+ | 0.2615 | 4.35 | 400 | 0.2693 | 0.8966 | 0.8966 |
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+ | 0.2455 | 6.52 | 600 | 0.2684 | 0.9027 | 0.9028 |
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+ | 0.2352 | 8.7 | 800 | 0.2805 | 0.8941 | 0.8939 |
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+ | 0.2138 | 10.87 | 1000 | 0.2761 | 0.8947 | 0.8946 |
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+ | 0.2049 | 13.04 | 1200 | 0.2838 | 0.8947 | 0.8946 |
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+ | 0.187 | 15.22 | 1400 | 0.2915 | 0.8947 | 0.8946 |
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+ | 0.172 | 17.39 | 1600 | 0.3155 | 0.8902 | 0.8898 |
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+ | 0.1588 | 19.57 | 1800 | 0.3204 | 0.8877 | 0.8877 |
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+ | 0.1468 | 21.74 | 2000 | 0.3266 | 0.8845 | 0.8843 |
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+ | 0.1319 | 23.91 | 2200 | 0.3453 | 0.8796 | 0.8795 |
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+ | 0.1229 | 26.09 | 2400 | 0.3427 | 0.8773 | 0.8775 |
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+ | 0.1106 | 28.26 | 2600 | 0.3987 | 0.8792 | 0.8795 |
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+ | 0.0982 | 30.43 | 2800 | 0.4070 | 0.8755 | 0.8754 |
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+ | 0.0862 | 32.61 | 3000 | 0.4562 | 0.8757 | 0.8761 |
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+ | 0.0801 | 34.78 | 3200 | 0.4331 | 0.8803 | 0.8802 |
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+ | 0.0736 | 36.96 | 3400 | 0.4788 | 0.8724 | 0.8727 |
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+ | 0.0631 | 39.13 | 3600 | 0.5258 | 0.8651 | 0.8652 |
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+ | 0.0566 | 41.3 | 3800 | 0.5171 | 0.8741 | 0.8741 |
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+ | 0.0535 | 43.48 | 4000 | 0.5513 | 0.8626 | 0.8624 |
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+ | 0.0484 | 45.65 | 4200 | 0.5790 | 0.8693 | 0.8700 |
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+ | 0.0444 | 47.83 | 4400 | 0.6137 | 0.8707 | 0.8706 |
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+ | 0.041 | 50.0 | 4600 | 0.6488 | 0.8736 | 0.8741 |
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+ | 0.0412 | 52.17 | 4800 | 0.6552 | 0.8739 | 0.8741 |
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+ | 0.0336 | 54.35 | 5000 | 0.6804 | 0.8722 | 0.8727 |
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+ | 0.0355 | 56.52 | 5200 | 0.6545 | 0.8743 | 0.8741 |
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+ | 0.033 | 58.7 | 5400 | 0.6452 | 0.8725 | 0.8727 |
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+ | 0.0274 | 60.87 | 5600 | 0.6867 | 0.8798 | 0.8795 |
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+ | 0.0294 | 63.04 | 5800 | 0.6560 | 0.8784 | 0.8782 |
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+ | 0.0287 | 65.22 | 6000 | 0.6701 | 0.8878 | 0.8877 |
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+ | 0.0226 | 67.39 | 6200 | 0.6983 | 0.8748 | 0.8747 |
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+ | 0.0266 | 69.57 | 6400 | 0.6277 | 0.8829 | 0.8830 |
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+ | 0.0245 | 71.74 | 6600 | 0.7203 | 0.8772 | 0.8775 |
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+ | 0.0231 | 73.91 | 6800 | 0.7011 | 0.8754 | 0.8754 |
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+ | 0.0205 | 76.09 | 7000 | 0.7072 | 0.8795 | 0.8795 |
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+ | 0.0198 | 78.26 | 7200 | 0.7095 | 0.8733 | 0.8734 |
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+ | 0.0217 | 80.43 | 7400 | 0.7206 | 0.8803 | 0.8802 |
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+ | 0.0194 | 82.61 | 7600 | 0.7410 | 0.8759 | 0.8761 |
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+ | 0.021 | 84.78 | 7800 | 0.7345 | 0.8788 | 0.8789 |
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+ | 0.018 | 86.96 | 8000 | 0.7149 | 0.8755 | 0.8754 |
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+ | 0.0171 | 89.13 | 8200 | 0.7380 | 0.8761 | 0.8761 |
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+ | 0.0169 | 91.3 | 8400 | 0.7260 | 0.8766 | 0.8768 |
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+ | 0.0142 | 93.48 | 8600 | 0.7683 | 0.8725 | 0.8727 |
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+ | 0.0141 | 95.65 | 8800 | 0.7640 | 0.8803 | 0.8802 |
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+ | 0.0141 | 97.83 | 9000 | 0.7762 | 0.8776 | 0.8775 |
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+ | 0.0126 | 100.0 | 9200 | 0.8161 | 0.8768 | 0.8768 |
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+ | 0.0146 | 102.17 | 9400 | 0.8132 | 0.8787 | 0.8789 |
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+ | 0.0121 | 104.35 | 9600 | 0.8014 | 0.8754 | 0.8754 |
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+ | 0.0118 | 106.52 | 9800 | 0.8046 | 0.8794 | 0.8795 |
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+ | 0.0145 | 108.7 | 10000 | 0.8003 | 0.8787 | 0.8789 |
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  ### Framework versions
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