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Kamer
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NoDuplicates

Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
text-embeddings-inference
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End of training

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  1. README.md +33 -28
  2. pytorch_model.bin +1 -1
README.md CHANGED
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  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.3927
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- - Accuracy: 0.9177
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- - F1 Macro: 0.8626
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- - F1 Class 0: 0.9449
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- - F1 Class 1: 0.5714
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- - F1 Class 2: 0.9130
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- - F1 Class 3: 0.7586
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- - F1 Class 4: 0.8098
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- - F1 Class 5: 0.7805
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- - F1 Class 6: 0.8927
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- - F1 Class 7: 0.7500
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  - F1 Class 8: 0.7500
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- - F1 Class 9: 0.9893
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- - F1 Class 10: 0.8991
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- - F1 Class 11: 0.9600
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- - F1 Class 12: 0.9202
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- - F1 Class 13: 0.9388
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- - F1 Class 14: 0.8941
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- - F1 Class 15: 0.8539
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- - F1 Class 16: 0.8387
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- - F1 Class 17: 0.9822
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- - F1 Class 18: 0.9583
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- - F1 Class 19: 0.8462
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  ## Model description
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@@ -70,12 +70,17 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Class 0 | F1 Class 1 | F1 Class 2 | F1 Class 3 | F1 Class 4 | F1 Class 5 | F1 Class 6 | F1 Class 7 | F1 Class 8 | F1 Class 9 | F1 Class 10 | F1 Class 11 | F1 Class 12 | F1 Class 13 | F1 Class 14 | F1 Class 15 | F1 Class 16 | F1 Class 17 | F1 Class 18 | F1 Class 19 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|
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- | 1.193 | 0.44 | 500 | 0.5958 | 0.8531 | 0.6517 | 0.8756 | 0.0 | 0.8770 | 0.0 | 0.7245 | 0.6667 | 0.8094 | 0.0 | 0.0 | 0.9862 | 0.7629 | 0.9600 | 0.8840 | 0.8936 | 0.5970 | 0.7089 | 0.7045 | 0.9455 | 0.9556 | 0.6829 |
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- | 0.5371 | 0.88 | 1000 | 0.4463 | 0.8863 | 0.7428 | 0.9138 | 0.0 | 0.8810 | 0.3478 | 0.7988 | 0.8293 | 0.8641 | 0.6154 | 0.0 | 0.9862 | 0.85 | 0.9600 | 0.9010 | 0.9167 | 0.8052 | 0.7253 | 0.7727 | 0.9643 | 0.92 | 0.8039 |
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- | 0.3813 | 1.33 | 1500 | 0.4335 | 0.8942 | 0.7760 | 0.9209 | 0.3333 | 0.8978 | 0.56 | 0.7771 | 0.7805 | 0.8389 | 0.5517 | 0.0 | 0.9877 | 0.9159 | 0.9600 | 0.9184 | 0.9388 | 0.8163 | 0.7551 | 0.8155 | 0.9765 | 0.9574 | 0.8190 |
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- | 0.3434 | 1.77 | 2000 | 0.4025 | 0.9040 | 0.8490 | 0.9354 | 0.5714 | 0.8939 | 0.8125 | 0.7829 | 0.8 | 0.8638 | 0.7463 | 0.7500 | 0.9877 | 0.8276 | 0.9600 | 0.9165 | 0.9388 | 0.7568 | 0.8276 | 0.8317 | 0.9759 | 0.9583 | 0.8431 |
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- | 0.2959 | 2.21 | 2500 | 0.4101 | 0.9080 | 0.8560 | 0.9329 | 0.5714 | 0.9077 | 0.8387 | 0.7905 | 0.8 | 0.8581 | 0.6984 | 0.7500 | 0.9892 | 0.9020 | 0.9600 | 0.9163 | 0.9388 | 0.8667 | 0.8511 | 0.8261 | 0.9759 | 0.9684 | 0.7778 |
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- | 0.1972 | 2.65 | 3000 | 0.3935 | 0.9173 | 0.8603 | 0.9418 | 0.5714 | 0.9176 | 0.7333 | 0.8232 | 0.7805 | 0.8866 | 0.7606 | 0.7500 | 0.9892 | 0.9074 | 0.9600 | 0.9240 | 0.9388 | 0.8837 | 0.8352 | 0.8511 | 0.9822 | 0.9583 | 0.8108 |
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.4279
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+ - Accuracy: 0.9128
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+ - F1 Macro: 0.8384
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+ - F1 Class 0: 0.9406
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+ - F1 Class 1: 0.3333
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+ - F1 Class 2: 0.9127
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+ - F1 Class 3: 0.6471
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+ - F1 Class 4: 0.8254
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+ - F1 Class 5: 0.8293
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+ - F1 Class 6: 0.8767
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+ - F1 Class 7: 0.7606
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  - F1 Class 8: 0.7500
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+ - F1 Class 9: 0.9878
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+ - F1 Class 10: 0.9444
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+ - F1 Class 11: 0.9630
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+ - F1 Class 12: 0.9265
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+ - F1 Class 13: 0.8980
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+ - F1 Class 14: 0.8444
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+ - F1 Class 15: 0.8132
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+ - F1 Class 16: 0.7778
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+ - F1 Class 17: 0.9651
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+ - F1 Class 18: 0.9574
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+ - F1 Class 19: 0.8148
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Class 0 | F1 Class 1 | F1 Class 2 | F1 Class 3 | F1 Class 4 | F1 Class 5 | F1 Class 6 | F1 Class 7 | F1 Class 8 | F1 Class 9 | F1 Class 10 | F1 Class 11 | F1 Class 12 | F1 Class 13 | F1 Class 14 | F1 Class 15 | F1 Class 16 | F1 Class 17 | F1 Class 18 | F1 Class 19 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|
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+ | 1.4862 | 0.27 | 300 | 0.8201 | 0.7845 | 0.4484 | 0.8675 | 0.0 | 0.8627 | 0.0 | 0.6733 | 0.0 | 0.6627 | 0.0 | 0.0 | 0.9862 | 0.1935 | 0.9600 | 0.8299 | 0.0833 | 0.2353 | 0.24 | 0.0400 | 0.8852 | 0.9451 | 0.5033 |
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+ | 0.7269 | 0.53 | 600 | 0.5951 | 0.8491 | 0.6504 | 0.9048 | 0.0 | 0.8567 | 0.0 | 0.7596 | 0.6111 | 0.6887 | 0.0 | 0.0 | 0.9877 | 0.8033 | 0.9286 | 0.8798 | 0.9167 | 0.74 | 0.6857 | 0.5823 | 0.9506 | 0.9485 | 0.7640 |
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+ | 0.5429 | 0.8 | 900 | 0.5375 | 0.8637 | 0.7086 | 0.8904 | 0.0 | 0.8589 | 0.0 | 0.7254 | 0.7805 | 0.8215 | 0.6769 | 0.0 | 0.9877 | 0.7833 | 1.0 | 0.9022 | 0.9130 | 0.7912 | 0.7733 | 0.7048 | 0.9032 | 0.9474 | 0.7119 |
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+ | 0.4594 | 1.06 | 1200 | 0.5110 | 0.8805 | 0.7113 | 0.9099 | 0.0 | 0.8925 | 0.0 | 0.7706 | 0.7391 | 0.8139 | 0.4091 | 0.0 | 0.9908 | 0.8785 | 1.0 | 0.8983 | 0.8936 | 0.8090 | 0.7556 | 0.7907 | 0.9529 | 0.9574 | 0.7647 |
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+ | 0.3484 | 1.33 | 1500 | 0.4679 | 0.8951 | 0.7667 | 0.9180 | 0.0 | 0.9080 | 0.6957 | 0.8 | 0.7619 | 0.8299 | 0.6875 | 0.0 | 0.9908 | 0.8909 | 1.0 | 0.9196 | 0.9130 | 0.8172 | 0.7865 | 0.7527 | 0.9398 | 0.9474 | 0.7755 |
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+ | 0.3744 | 1.59 | 1800 | 0.4359 | 0.8951 | 0.7774 | 0.9290 | 0.0 | 0.8815 | 0.8462 | 0.8049 | 0.7805 | 0.8449 | 0.7059 | 0.0 | 0.9908 | 0.9346 | 1.0 | 0.9143 | 0.8980 | 0.8387 | 0.7475 | 0.7179 | 0.9647 | 0.9583 | 0.7895 |
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+ | 0.3514 | 1.86 | 2100 | 0.5161 | 0.8903 | 0.7592 | 0.9109 | 0.0 | 0.8973 | 0.6429 | 0.7603 | 0.7907 | 0.8571 | 0.7077 | 0.0 | 0.9908 | 0.9346 | 1.0 | 0.8971 | 0.8936 | 0.7042 | 0.7324 | 0.7857 | 0.9595 | 0.9574 | 0.7609 |
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+ | 0.3111 | 2.12 | 2400 | 0.4327 | 0.9080 | 0.8027 | 0.9283 | 0.3333 | 0.9141 | 0.7407 | 0.8207 | 0.8095 | 0.8622 | 0.7606 | 0.0 | 0.9908 | 0.9298 | 0.9630 | 0.9215 | 0.9167 | 0.8041 | 0.8 | 0.8132 | 0.9651 | 0.9574 | 0.8224 |
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+ | 0.2088 | 2.39 | 2700 | 0.4356 | 0.9128 | 0.8452 | 0.9386 | 0.3333 | 0.9058 | 0.8462 | 0.8265 | 0.8 | 0.8562 | 0.7429 | 0.7500 | 0.9893 | 0.9346 | 0.9630 | 0.9322 | 0.8936 | 0.8205 | 0.8372 | 0.7765 | 0.9651 | 0.9574 | 0.8350 |
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+ | 0.2317 | 2.65 | 3000 | 0.4294 | 0.9137 | 0.8217 | 0.9365 | 0.3333 | 0.9102 | 0.625 | 0.8243 | 0.8293 | 0.875 | 0.8056 | 0.3333 | 0.9893 | 0.9444 | 0.9630 | 0.9284 | 0.8980 | 0.8478 | 0.8471 | 0.7816 | 0.9651 | 0.9574 | 0.8400 |
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+ | 0.1816 | 2.92 | 3300 | 0.4279 | 0.9128 | 0.8384 | 0.9406 | 0.3333 | 0.9127 | 0.6471 | 0.8254 | 0.8293 | 0.8767 | 0.7606 | 0.7500 | 0.9878 | 0.9444 | 0.9630 | 0.9265 | 0.8980 | 0.8444 | 0.8132 | 0.7778 | 0.9651 | 0.9574 | 0.8148 |
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  ### Framework versions
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