--- license: mit base_model: microsoft/Multilingual-MiniLM-L12-H384 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: ner-coin-v2 results: [] --- # ner-coin-v2 This model is a fine-tuned version of [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0182 - Precision: 0.9837 - Recall: 0.9947 - F1: 0.9892 - Accuracy: 0.9971 ## 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: 5e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 51 | 0.2140 | 0.7507 | 0.8582 | 0.8008 | 0.9790 | | No log | 2.0 | 102 | 0.1401 | 0.9631 | 0.9790 | 0.9710 | 0.9936 | | No log | 3.0 | 153 | 0.1071 | 0.9710 | 0.9790 | 0.9750 | 0.9945 | | No log | 4.0 | 204 | 0.0852 | 0.9676 | 0.9857 | 0.9766 | 0.9948 | | No log | 5.0 | 255 | 0.0712 | 0.9741 | 0.9880 | 0.9810 | 0.9953 | | No log | 6.0 | 306 | 0.0586 | 0.9842 | 0.9820 | 0.9831 | 0.9962 | | No log | 7.0 | 357 | 0.0514 | 0.9799 | 0.9865 | 0.9832 | 0.9960 | | No log | 8.0 | 408 | 0.0472 | 0.9778 | 0.9895 | 0.9836 | 0.9957 | | No log | 9.0 | 459 | 0.0428 | 0.9749 | 0.9895 | 0.9821 | 0.9956 | | 0.1133 | 10.0 | 510 | 0.0393 | 0.9864 | 0.9782 | 0.9823 | 0.9956 | | 0.1133 | 11.0 | 561 | 0.0342 | 0.9828 | 0.9857 | 0.9843 | 0.9959 | | 0.1133 | 12.0 | 612 | 0.0307 | 0.9806 | 0.9880 | 0.9843 | 0.9964 | | 0.1133 | 13.0 | 663 | 0.0302 | 0.9748 | 0.9880 | 0.9814 | 0.9956 | | 0.1133 | 14.0 | 714 | 0.0295 | 0.9677 | 0.9895 | 0.9785 | 0.9948 | | 0.1133 | 15.0 | 765 | 0.0256 | 0.9828 | 0.9872 | 0.9850 | 0.9964 | | 0.1133 | 16.0 | 816 | 0.0295 | 0.9601 | 0.9932 | 0.9764 | 0.9939 | | 0.1133 | 17.0 | 867 | 0.0252 | 0.9784 | 0.9865 | 0.9824 | 0.9958 | | 0.1133 | 18.0 | 918 | 0.0289 | 0.9819 | 0.9775 | 0.9797 | 0.9948 | | 0.1133 | 19.0 | 969 | 0.0248 | 0.9798 | 0.9842 | 0.9820 | 0.9954 | | 0.0217 | 20.0 | 1020 | 0.0254 | 0.9741 | 0.9880 | 0.9810 | 0.9950 | | 0.0217 | 21.0 | 1071 | 0.0219 | 0.9749 | 0.9902 | 0.9825 | 0.9956 | | 0.0217 | 22.0 | 1122 | 0.0240 | 0.9770 | 0.9887 | 0.9828 | 0.9955 | | 0.0217 | 23.0 | 1173 | 0.0226 | 0.9807 | 0.9887 | 0.9847 | 0.9958 | | 0.0217 | 24.0 | 1224 | 0.0209 | 0.9756 | 0.9910 | 0.9833 | 0.9957 | | 0.0217 | 25.0 | 1275 | 0.0203 | 0.9822 | 0.9917 | 0.9869 | 0.9963 | | 0.0217 | 26.0 | 1326 | 0.0231 | 0.9727 | 0.9902 | 0.9814 | 0.9950 | | 0.0217 | 27.0 | 1377 | 0.0204 | 0.9778 | 0.9895 | 0.9836 | 0.9958 | | 0.0217 | 28.0 | 1428 | 0.0196 | 0.9771 | 0.9917 | 0.9844 | 0.9962 | | 0.0217 | 29.0 | 1479 | 0.0206 | 0.9757 | 0.9932 | 0.9844 | 0.9957 | | 0.0097 | 30.0 | 1530 | 0.0217 | 0.9757 | 0.9955 | 0.9855 | 0.9959 | | 0.0097 | 31.0 | 1581 | 0.0192 | 0.9843 | 0.9872 | 0.9858 | 0.9962 | | 0.0097 | 32.0 | 1632 | 0.0189 | 0.9844 | 0.9910 | 0.9877 | 0.9964 | | 0.0097 | 33.0 | 1683 | 0.0174 | 0.9844 | 0.9925 | 0.9884 | 0.9966 | | 0.0097 | 34.0 | 1734 | 0.0183 | 0.9836 | 0.9910 | 0.9873 | 0.9966 | | 0.0097 | 35.0 | 1785 | 0.0189 | 0.9785 | 0.9917 | 0.9851 | 0.9964 | | 0.0097 | 36.0 | 1836 | 0.0202 | 0.9757 | 0.9940 | 0.9848 | 0.9960 | | 0.0097 | 37.0 | 1887 | 0.0203 | 0.9770 | 0.9880 | 0.9825 | 0.9957 | | 0.0097 | 38.0 | 1938 | 0.0189 | 0.9778 | 0.9932 | 0.9855 | 0.9962 | | 0.0097 | 39.0 | 1989 | 0.0169 | 0.9836 | 0.9895 | 0.9865 | 0.9966 | | 0.0055 | 40.0 | 2040 | 0.0183 | 0.9778 | 0.9917 | 0.9847 | 0.9961 | | 0.0055 | 41.0 | 2091 | 0.0159 | 0.9866 | 0.9910 | 0.9888 | 0.9968 | | 0.0055 | 42.0 | 2142 | 0.0175 | 0.9778 | 0.9917 | 0.9847 | 0.9962 | | 0.0055 | 43.0 | 2193 | 0.0153 | 0.9829 | 0.9940 | 0.9884 | 0.9969 | | 0.0055 | 44.0 | 2244 | 0.0170 | 0.9778 | 0.9925 | 0.9851 | 0.9963 | | 0.0055 | 45.0 | 2295 | 0.0184 | 0.9750 | 0.9940 | 0.9844 | 0.9962 | | 0.0055 | 46.0 | 2346 | 0.0172 | 0.9786 | 0.9940 | 0.9862 | 0.9964 | | 0.0055 | 47.0 | 2397 | 0.0174 | 0.9779 | 0.9947 | 0.9862 | 0.9965 | | 0.0055 | 48.0 | 2448 | 0.0169 | 0.9778 | 0.9910 | 0.9844 | 0.9962 | | 0.0055 | 49.0 | 2499 | 0.0193 | 0.9701 | 0.9962 | 0.9830 | 0.9958 | | 0.0035 | 50.0 | 2550 | 0.0163 | 0.9792 | 0.9910 | 0.9851 | 0.9963 | | 0.0035 | 51.0 | 2601 | 0.0173 | 0.9771 | 0.9925 | 0.9847 | 0.9960 | | 0.0035 | 52.0 | 2652 | 0.0164 | 0.9829 | 0.9932 | 0.9881 | 0.9966 | | 0.0035 | 53.0 | 2703 | 0.0177 | 0.9757 | 0.9955 | 0.9855 | 0.9961 | | 0.0035 | 54.0 | 2754 | 0.0164 | 0.9815 | 0.9932 | 0.9873 | 0.9965 | | 0.0035 | 55.0 | 2805 | 0.0171 | 0.9793 | 0.9947 | 0.9870 | 0.9966 | | 0.0035 | 56.0 | 2856 | 0.0175 | 0.98 | 0.9925 | 0.9862 | 0.9966 | | 0.0035 | 57.0 | 2907 | 0.0167 | 0.9801 | 0.9955 | 0.9877 | 0.9966 | | 0.0035 | 58.0 | 2958 | 0.0168 | 0.9880 | 0.9887 | 0.9884 | 0.9966 | | 0.0025 | 59.0 | 3009 | 0.0174 | 0.9858 | 0.9917 | 0.9888 | 0.9969 | | 0.0025 | 60.0 | 3060 | 0.0153 | 0.9837 | 0.9940 | 0.9888 | 0.9970 | | 0.0025 | 61.0 | 3111 | 0.0165 | 0.9829 | 0.9932 | 0.9881 | 0.9968 | | 0.0025 | 62.0 | 3162 | 0.0150 | 0.9881 | 0.9925 | 0.9903 | 0.9971 | | 0.0025 | 63.0 | 3213 | 0.0156 | 0.9851 | 0.9947 | 0.9899 | 0.9972 | | 0.0025 | 64.0 | 3264 | 0.0147 | 0.9873 | 0.9940 | 0.9907 | 0.9974 | | 0.0025 | 65.0 | 3315 | 0.0169 | 0.9815 | 0.9947 | 0.9881 | 0.9967 | | 0.0025 | 66.0 | 3366 | 0.0186 | 0.9786 | 0.9962 | 0.9874 | 0.9964 | | 0.0025 | 67.0 | 3417 | 0.0171 | 0.9815 | 0.9940 | 0.9877 | 0.9967 | | 0.0025 | 68.0 | 3468 | 0.0164 | 0.9822 | 0.9932 | 0.9877 | 0.9966 | | 0.0021 | 69.0 | 3519 | 0.0161 | 0.9829 | 0.9932 | 0.9881 | 0.9968 | | 0.0021 | 70.0 | 3570 | 0.0156 | 0.9858 | 0.9925 | 0.9892 | 0.9970 | | 0.0021 | 71.0 | 3621 | 0.0163 | 0.9815 | 0.9947 | 0.9881 | 0.9967 | | 0.0021 | 72.0 | 3672 | 0.0166 | 0.9837 | 0.9947 | 0.9892 | 0.9970 | | 0.0021 | 73.0 | 3723 | 0.0161 | 0.9866 | 0.9925 | 0.9895 | 0.9970 | | 0.0021 | 74.0 | 3774 | 0.0165 | 0.9837 | 0.9947 | 0.9892 | 0.9970 | | 0.0021 | 75.0 | 3825 | 0.0165 | 0.9859 | 0.9947 | 0.9903 | 0.9972 | | 0.0021 | 76.0 | 3876 | 0.0170 | 0.9830 | 0.9947 | 0.9888 | 0.9969 | | 0.0021 | 77.0 | 3927 | 0.0171 | 0.9844 | 0.9947 | 0.9896 | 0.9971 | | 0.0021 | 78.0 | 3978 | 0.0179 | 0.9815 | 0.9947 | 0.9881 | 0.9967 | | 0.0016 | 79.0 | 4029 | 0.0170 | 0.9851 | 0.9947 | 0.9899 | 0.9971 | | 0.0016 | 80.0 | 4080 | 0.0170 | 0.9851 | 0.9947 | 0.9899 | 0.9971 | | 0.0016 | 81.0 | 4131 | 0.0186 | 0.9779 | 0.9955 | 0.9866 | 0.9963 | | 0.0016 | 82.0 | 4182 | 0.0179 | 0.9822 | 0.9947 | 0.9884 | 0.9968 | | 0.0016 | 83.0 | 4233 | 0.0177 | 0.9822 | 0.9947 | 0.9884 | 0.9968 | | 0.0016 | 84.0 | 4284 | 0.0177 | 0.9822 | 0.9947 | 0.9884 | 0.9968 | | 0.0016 | 85.0 | 4335 | 0.0176 | 0.9830 | 0.9947 | 0.9888 | 0.9969 | | 0.0016 | 86.0 | 4386 | 0.0182 | 0.9822 | 0.9947 | 0.9884 | 0.9968 | | 0.0016 | 87.0 | 4437 | 0.0173 | 0.9851 | 0.9947 | 0.9899 | 0.9971 | | 0.0016 | 88.0 | 4488 | 0.0179 | 0.9808 | 0.9947 | 0.9877 | 0.9966 | | 0.0015 | 89.0 | 4539 | 0.0176 | 0.9837 | 0.9947 | 0.9892 | 0.9970 | | 0.0015 | 90.0 | 4590 | 0.0181 | 0.9837 | 0.9947 | 0.9892 | 0.9970 | | 0.0015 | 91.0 | 4641 | 0.0183 | 0.9837 | 0.9947 | 0.9892 | 0.9970 | | 0.0015 | 92.0 | 4692 | 0.0183 | 0.9844 | 0.9947 | 0.9896 | 0.9971 | | 0.0015 | 93.0 | 4743 | 0.0188 | 0.9837 | 0.9947 | 0.9892 | 0.9969 | | 0.0015 | 94.0 | 4794 | 0.0189 | 0.9837 | 0.9947 | 0.9892 | 0.9969 | | 0.0015 | 95.0 | 4845 | 0.0186 | 0.9837 | 0.9947 | 0.9892 | 0.9969 | | 0.0015 | 96.0 | 4896 | 0.0180 | 0.9837 | 0.9947 | 0.9892 | 0.9971 | | 0.0015 | 97.0 | 4947 | 0.0181 | 0.9837 | 0.9947 | 0.9892 | 0.9970 | | 0.0015 | 98.0 | 4998 | 0.0182 | 0.9837 | 0.9947 | 0.9892 | 0.9970 | | 0.0013 | 99.0 | 5049 | 0.0182 | 0.9837 | 0.9947 | 0.9892 | 0.9971 | | 0.0013 | 100.0 | 5100 | 0.0182 | 0.9837 | 0.9947 | 0.9892 | 0.9971 | ### Framework versions - Transformers 4.40.2 - Pytorch 2.1.0+cu121 - Datasets 2.14.5 - Tokenizers 0.19.1