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levshechter
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tibetan_code_switching_model

Token Classification
Transformers
Safetensors
bert
Generated from Trainer
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xet
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  1. README.md +25 -25
  2. final_model/model.safetensors +1 -1
README.md CHANGED
@@ -17,16 +17,16 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [OMRIDRORI/mbert-tibetan-continual-unicode-240k](https://huggingface.co/OMRIDRORI/mbert-tibetan-continual-unicode-240k) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2587
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- - Accuracy: 0.9580
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- - Switch Precision: 0.9386
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- - Switch Recall: 0.9817
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- - Switch F1: 0.9596
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  - True Switches: 109
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- - Pred Switches: 114
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- - Tp: 107
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  - Fp: 7
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- - Fn: 2
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  ## Model description
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@@ -57,23 +57,23 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Accuracy | Switch Precision | Switch Recall | Switch F1 | True Switches | Pred Switches | Tp | Fp | Fn |
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- |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------------:|:-------------:|:---------:|:-------------:|:-------------:|:---:|:---:|:--:|
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- | 4.4519 | 1.0 | 50 | 4.8563 | 0.2905 | 0.0906 | 0.9083 | 0.1647 | 109 | 1093 | 99 | 994 | 10 |
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- | 2.0058 | 2.0 | 100 | 1.4230 | 0.5367 | 0.5376 | 0.9174 | 0.6780 | 109 | 186 | 100 | 86 | 9 |
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- | 1.249 | 3.0 | 150 | 1.0507 | 0.6400 | 0.6429 | 0.9908 | 0.7798 | 109 | 168 | 108 | 60 | 1 |
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- | 0.885 | 4.0 | 200 | 0.8003 | 0.7341 | 0.6488 | 1.0 | 0.7870 | 109 | 168 | 109 | 59 | 0 |
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- | 0.734 | 5.0 | 250 | 0.7127 | 0.7770 | 0.6606 | 1.0 | 0.7956 | 109 | 165 | 109 | 56 | 0 |
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- | 0.5639 | 6.0 | 300 | 0.6443 | 0.8205 | 0.6728 | 1.0 | 0.8044 | 109 | 162 | 109 | 53 | 0 |
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- | 0.3557 | 7.0 | 350 | 0.3794 | 0.9025 | 0.7219 | 1.0 | 0.8385 | 109 | 151 | 109 | 42 | 0 |
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- | 0.249 | 8.0 | 400 | 0.2706 | 0.9363 | 0.8120 | 0.9908 | 0.8926 | 109 | 133 | 108 | 25 | 1 |
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- | 0.1682 | 9.0 | 450 | 0.2421 | 0.9344 | 0.9 | 0.9908 | 0.9432 | 109 | 120 | 108 | 12 | 1 |
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- | 0.0818 | 10.0 | 500 | 0.2537 | 0.9368 | 0.8689 | 0.9725 | 0.9177 | 109 | 122 | 106 | 16 | 3 |
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- | 0.0632 | 11.0 | 550 | 0.3495 | 0.9426 | 0.8917 | 0.9817 | 0.9345 | 109 | 120 | 107 | 13 | 2 |
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- | 0.0227 | 12.0 | 600 | 0.2392 | 0.9537 | 0.9304 | 0.9817 | 0.9554 | 109 | 115 | 107 | 8 | 2 |
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- | 0.0246 | 13.0 | 650 | 0.3026 | 0.9488 | 0.8843 | 0.9817 | 0.9304 | 109 | 121 | 107 | 14 | 2 |
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- | 0.0146 | 14.0 | 700 | 0.2463 | 0.9604 | 0.9386 | 0.9817 | 0.9596 | 109 | 114 | 107 | 7 | 2 |
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- | 0.0178 | 15.0 | 750 | 0.2587 | 0.9580 | 0.9386 | 0.9817 | 0.9596 | 109 | 114 | 107 | 7 | 2 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [OMRIDRORI/mbert-tibetan-continual-unicode-240k](https://huggingface.co/OMRIDRORI/mbert-tibetan-continual-unicode-240k) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.3714
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+ - Accuracy: 0.9445
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+ - Switch Precision: 0.9381
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+ - Switch Recall: 0.9725
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+ - Switch F1: 0.9550
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  - True Switches: 109
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+ - Pred Switches: 113
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+ - Tp: 106
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  - Fp: 7
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+ - Fn: 3
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  ## Model description
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Switch Precision | Switch Recall | Switch F1 | True Switches | Pred Switches | Tp | Fp | Fn |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------------:|:-------------:|:---------:|:-------------:|:-------------:|:---:|:----:|:--:|
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+ | 4.727 | 1.0 | 50 | 5.6020 | 0.2133 | 0.0749 | 0.9908 | 0.1393 | 109 | 1442 | 108 | 1334 | 1 |
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+ | 1.7867 | 2.0 | 100 | 1.2875 | 0.5304 | 0.5365 | 0.9450 | 0.6844 | 109 | 192 | 103 | 89 | 6 |
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+ | 1.1232 | 3.0 | 150 | 0.9034 | 0.6356 | 0.6835 | 0.9908 | 0.8090 | 109 | 158 | 108 | 50 | 1 |
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+ | 0.7733 | 4.0 | 200 | 0.6946 | 0.7664 | 0.6855 | 1.0 | 0.8134 | 109 | 159 | 109 | 50 | 0 |
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+ | 0.5582 | 5.0 | 250 | 0.4907 | 0.8514 | 0.7219 | 1.0 | 0.8385 | 109 | 151 | 109 | 42 | 0 |
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+ | 0.4037 | 6.0 | 300 | 0.4428 | 0.8808 | 0.7219 | 1.0 | 0.8385 | 109 | 151 | 109 | 42 | 0 |
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+ | 0.2593 | 7.0 | 350 | 0.3282 | 0.9213 | 0.8134 | 1.0 | 0.8971 | 109 | 134 | 109 | 25 | 0 |
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+ | 0.1714 | 8.0 | 400 | 0.3918 | 0.9044 | 0.8244 | 0.9908 | 0.9000 | 109 | 131 | 108 | 23 | 1 |
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+ | 0.1114 | 9.0 | 450 | 0.3473 | 0.9334 | 0.8710 | 0.9908 | 0.9270 | 109 | 124 | 108 | 16 | 1 |
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+ | 0.0678 | 10.0 | 500 | 0.3275 | 0.9348 | 0.9145 | 0.9817 | 0.9469 | 109 | 117 | 107 | 10 | 2 |
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+ | 0.075 | 11.0 | 550 | 0.3718 | 0.9392 | 0.8917 | 0.9817 | 0.9345 | 109 | 120 | 107 | 13 | 2 |
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+ | 0.0276 | 12.0 | 600 | 0.3588 | 0.9435 | 0.9068 | 0.9817 | 0.9427 | 109 | 118 | 107 | 11 | 2 |
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+ | 0.0251 | 13.0 | 650 | 0.3488 | 0.9411 | 0.9304 | 0.9817 | 0.9554 | 109 | 115 | 107 | 8 | 2 |
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+ | 0.0154 | 14.0 | 700 | 0.3599 | 0.9455 | 0.9386 | 0.9817 | 0.9596 | 109 | 114 | 107 | 7 | 2 |
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+ | 0.0165 | 15.0 | 750 | 0.3714 | 0.9445 | 0.9381 | 0.9725 | 0.9550 | 109 | 113 | 106 | 7 | 3 |
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  ### Framework versions
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