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lilt
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End of training

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  1. README.md +45 -44
  2. model.safetensors +1 -1
  3. runs/Mar18_15-04-13_59b711eb0c8d/events.out.tfevents.1742310254.59b711eb0c8d.1222.0 +3 -0
  4. tokenizer.json +2 -2
  5. training_args.bin +1 -1
README.md CHANGED
@@ -21,11 +21,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [nielsr/lilt-xlm-roberta-base](https://huggingface.co/nielsr/lilt-xlm-roberta-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2603
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- - Precision: 0.8677
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- - Recall: 0.8977
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- - F1: 0.8825
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- - Accuracy: 0.9623
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  ## Model description
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@@ -56,47 +56,48 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | No log | 0.8333 | 100 | 0.3280 | 0.6504 | 0.7664 | 0.7037 | 0.8873 |
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- | No log | 1.6667 | 200 | 0.1495 | 0.8044 | 0.8583 | 0.8305 | 0.9423 |
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- | No log | 2.5 | 300 | 0.1681 | 0.8192 | 0.8894 | 0.8529 | 0.9486 |
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- | No log | 3.3333 | 400 | 0.1133 | 0.8581 | 0.8818 | 0.8698 | 0.9578 |
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- | 0.2697 | 4.1667 | 500 | 0.1116 | 0.8504 | 0.8798 | 0.8648 | 0.9575 |
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- | 0.2697 | 5.0 | 600 | 0.1329 | 0.8163 | 0.8846 | 0.8491 | 0.9510 |
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- | 0.2697 | 5.8333 | 700 | 0.1321 | 0.8524 | 0.8784 | 0.8652 | 0.9570 |
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- | 0.2697 | 6.6667 | 800 | 0.1271 | 0.8597 | 0.8853 | 0.8723 | 0.9591 |
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- | 0.2697 | 7.5 | 900 | 0.1245 | 0.8833 | 0.8839 | 0.8836 | 0.9630 |
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- | 0.0644 | 8.3333 | 1000 | 0.1519 | 0.8831 | 0.8977 | 0.8903 | 0.9644 |
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- | 0.0644 | 9.1667 | 1100 | 0.1324 | 0.8707 | 0.8887 | 0.8796 | 0.9609 |
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- | 0.0644 | 10.0 | 1200 | 0.1315 | 0.8803 | 0.8943 | 0.8872 | 0.9617 |
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- | 0.0644 | 10.8333 | 1300 | 0.1659 | 0.8672 | 0.8846 | 0.8758 | 0.9596 |
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- | 0.0644 | 11.6667 | 1400 | 0.1696 | 0.8646 | 0.8956 | 0.8798 | 0.9599 |
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- | 0.0312 | 12.5 | 1500 | 0.2016 | 0.8590 | 0.8970 | 0.8776 | 0.9596 |
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- | 0.0312 | 13.3333 | 1600 | 0.1947 | 0.8420 | 0.8763 | 0.8588 | 0.9552 |
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- | 0.0312 | 14.1667 | 1700 | 0.1909 | 0.8622 | 0.8735 | 0.8678 | 0.9573 |
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- | 0.0312 | 15.0 | 1800 | 0.2181 | 0.8633 | 0.8770 | 0.8701 | 0.9583 |
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- | 0.0312 | 15.8333 | 1900 | 0.2022 | 0.8706 | 0.8929 | 0.8816 | 0.9625 |
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- | 0.0153 | 16.6667 | 2000 | 0.2072 | 0.8535 | 0.8901 | 0.8714 | 0.9588 |
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- | 0.0153 | 17.5 | 2100 | 0.2023 | 0.8594 | 0.8915 | 0.8752 | 0.9596 |
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- | 0.0153 | 18.3333 | 2200 | 0.2033 | 0.8786 | 0.9005 | 0.8894 | 0.9649 |
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- | 0.0153 | 19.1667 | 2300 | 0.2130 | 0.8575 | 0.8901 | 0.8735 | 0.9591 |
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- | 0.0153 | 20.0 | 2400 | 0.2264 | 0.8768 | 0.8950 | 0.8858 | 0.9628 |
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- | 0.0053 | 20.8333 | 2500 | 0.2427 | 0.8673 | 0.8901 | 0.8786 | 0.9607 |
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- | 0.0053 | 21.6667 | 2600 | 0.2551 | 0.8595 | 0.8963 | 0.8775 | 0.9604 |
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- | 0.0053 | 22.5 | 2700 | 0.2497 | 0.8619 | 0.9012 | 0.8811 | 0.9620 |
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- | 0.0053 | 23.3333 | 2800 | 0.2585 | 0.8717 | 0.8922 | 0.8818 | 0.9609 |
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- | 0.0053 | 24.1667 | 2900 | 0.2549 | 0.8646 | 0.8915 | 0.8778 | 0.9602 |
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- | 0.0025 | 25.0 | 3000 | 0.2591 | 0.8662 | 0.8991 | 0.8823 | 0.9620 |
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- | 0.0025 | 25.8333 | 3100 | 0.2484 | 0.8655 | 0.8984 | 0.8817 | 0.9617 |
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- | 0.0025 | 26.6667 | 3200 | 0.2530 | 0.8654 | 0.8977 | 0.8813 | 0.9615 |
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- | 0.0025 | 27.5 | 3300 | 0.2563 | 0.8666 | 0.8936 | 0.8799 | 0.9615 |
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- | 0.0025 | 28.3333 | 3400 | 0.2564 | 0.8707 | 0.8936 | 0.8820 | 0.9625 |
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- | 0.0014 | 29.1667 | 3500 | 0.2598 | 0.8683 | 0.8977 | 0.8828 | 0.9623 |
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- | 0.0014 | 30.0 | 3600 | 0.2603 | 0.8677 | 0.8977 | 0.8825 | 0.9623 |
 
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  ### Framework versions
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  - Transformers 4.48.3
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- - Pytorch 2.5.1+cu124
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- - Datasets 3.3.2
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- - Tokenizers 0.21.0
 
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  This model is a fine-tuned version of [nielsr/lilt-xlm-roberta-base](https://huggingface.co/nielsr/lilt-xlm-roberta-base) on an unknown dataset.
23
  It achieves the following results on the evaluation set:
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+ - Loss: 0.2434
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+ - Precision: 0.9144
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+ - Recall: 0.9105
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+ - F1: 0.9124
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+ - Accuracy: 0.9725
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | No log | 0.7937 | 100 | 0.1878 | 0.8406 | 0.8761 | 0.8580 | 0.9542 |
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+ | No log | 1.5873 | 200 | 0.1337 | 0.8943 | 0.8864 | 0.8903 | 0.9650 |
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+ | No log | 2.3810 | 300 | 0.1259 | 0.9020 | 0.9214 | 0.9116 | 0.9716 |
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+ | No log | 3.1746 | 400 | 0.1317 | 0.9100 | 0.9181 | 0.9140 | 0.9730 |
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+ | 0.2107 | 3.9683 | 500 | 0.1159 | 0.9144 | 0.9065 | 0.9104 | 0.9710 |
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+ | 0.2107 | 4.7619 | 600 | 0.1169 | 0.9147 | 0.9072 | 0.9109 | 0.9715 |
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+ | 0.2107 | 5.5556 | 700 | 0.1240 | 0.9025 | 0.9144 | 0.9084 | 0.9712 |
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+ | 0.2107 | 6.3492 | 800 | 0.1351 | 0.9160 | 0.9118 | 0.9139 | 0.9727 |
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+ | 0.2107 | 7.1429 | 900 | 0.1469 | 0.9207 | 0.9055 | 0.9131 | 0.9722 |
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+ | 0.0518 | 7.9365 | 1000 | 0.1333 | 0.9053 | 0.9158 | 0.9105 | 0.9717 |
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+ | 0.0518 | 8.7302 | 1100 | 0.1367 | 0.9119 | 0.9167 | 0.9143 | 0.9724 |
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+ | 0.0518 | 9.5238 | 1200 | 0.1412 | 0.9057 | 0.9134 | 0.9095 | 0.9712 |
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+ | 0.0518 | 10.3175 | 1300 | 0.1666 | 0.9203 | 0.9158 | 0.9180 | 0.9740 |
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+ | 0.0518 | 11.1111 | 1400 | 0.1610 | 0.9050 | 0.9062 | 0.9056 | 0.9707 |
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+ | 0.0316 | 11.9048 | 1500 | 0.1677 | 0.9175 | 0.9111 | 0.9143 | 0.9720 |
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+ | 0.0316 | 12.6984 | 1600 | 0.1838 | 0.9097 | 0.9052 | 0.9074 | 0.9715 |
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+ | 0.0316 | 13.4921 | 1700 | 0.1622 | 0.9182 | 0.9082 | 0.9131 | 0.9725 |
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+ | 0.0316 | 14.2857 | 1800 | 0.1855 | 0.9161 | 0.9092 | 0.9126 | 0.9725 |
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+ | 0.0316 | 15.0794 | 1900 | 0.1739 | 0.9078 | 0.9171 | 0.9124 | 0.9725 |
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+ | 0.0174 | 15.8730 | 2000 | 0.1902 | 0.9167 | 0.9167 | 0.9167 | 0.9734 |
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+ | 0.0174 | 16.6667 | 2100 | 0.1729 | 0.9207 | 0.9171 | 0.9189 | 0.9739 |
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+ | 0.0174 | 17.4603 | 2200 | 0.2083 | 0.9147 | 0.9171 | 0.9159 | 0.9734 |
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+ | 0.0174 | 18.2540 | 2300 | 0.2233 | 0.9108 | 0.9177 | 0.9143 | 0.9724 |
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+ | 0.0174 | 19.0476 | 2400 | 0.2165 | 0.9201 | 0.9134 | 0.9168 | 0.9730 |
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+ | 0.0085 | 19.8413 | 2500 | 0.2138 | 0.9117 | 0.9111 | 0.9114 | 0.9721 |
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+ | 0.0085 | 20.6349 | 2600 | 0.2109 | 0.9150 | 0.9108 | 0.9129 | 0.9725 |
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+ | 0.0085 | 21.4286 | 2700 | 0.2118 | 0.9216 | 0.9167 | 0.9192 | 0.9742 |
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+ | 0.0085 | 22.2222 | 2800 | 0.2287 | 0.9184 | 0.9184 | 0.9184 | 0.9742 |
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+ | 0.0085 | 23.0159 | 2900 | 0.2350 | 0.9118 | 0.9085 | 0.9101 | 0.9719 |
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+ | 0.0043 | 23.8095 | 3000 | 0.2406 | 0.9109 | 0.9158 | 0.9133 | 0.9727 |
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+ | 0.0043 | 24.6032 | 3100 | 0.2480 | 0.9105 | 0.9072 | 0.9088 | 0.9715 |
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+ | 0.0043 | 25.3968 | 3200 | 0.2430 | 0.9112 | 0.9055 | 0.9084 | 0.9714 |
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+ | 0.0043 | 26.1905 | 3300 | 0.2396 | 0.9092 | 0.9068 | 0.9080 | 0.9712 |
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+ | 0.0043 | 26.9841 | 3400 | 0.2386 | 0.9152 | 0.9164 | 0.9158 | 0.9732 |
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+ | 0.0026 | 27.7778 | 3500 | 0.2417 | 0.9123 | 0.9111 | 0.9117 | 0.9720 |
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+ | 0.0026 | 28.5714 | 3600 | 0.2433 | 0.9136 | 0.9085 | 0.9110 | 0.9721 |
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+ | 0.0026 | 29.3651 | 3700 | 0.2434 | 0.9144 | 0.9105 | 0.9124 | 0.9725 |
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  ### Framework versions
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  - Transformers 4.48.3
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+ - Pytorch 2.6.0+cu124
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+ - Datasets 3.4.1
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+ - Tokenizers 0.21.1
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