--- license: mit base_model: microsoft/Multilingual-MiniLM-L12-H384 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: ner-coin-v3 results: [] --- # ner-coin-v3 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: 0.0147 - Precision: 0.9941 - Recall: 0.9931 - F1: 0.9936 - Accuracy: 0.9978 ## 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 | 52 | 0.1916 | 0.9775 | 0.9777 | 0.9776 | 0.9935 | | No log | 2.0 | 104 | 0.1443 | 0.9815 | 0.9823 | 0.9819 | 0.9948 | | No log | 3.0 | 156 | 0.1142 | 0.9844 | 0.9844 | 0.9844 | 0.9953 | | No log | 4.0 | 208 | 0.0938 | 0.9852 | 0.9834 | 0.9843 | 0.9952 | | No log | 5.0 | 260 | 0.0764 | 0.9870 | 0.9870 | 0.9870 | 0.9959 | | No log | 6.0 | 312 | 0.0649 | 0.9876 | 0.9874 | 0.9875 | 0.9962 | | No log | 7.0 | 364 | 0.0552 | 0.9903 | 0.9893 | 0.9898 | 0.9968 | | No log | 8.0 | 416 | 0.0500 | 0.9884 | 0.9876 | 0.988 | 0.9962 | | No log | 9.0 | 468 | 0.0443 | 0.9882 | 0.9878 | 0.9880 | 0.9963 | | 0.1249 | 10.0 | 520 | 0.0402 | 0.9882 | 0.9880 | 0.9881 | 0.9962 | | 0.1249 | 11.0 | 572 | 0.0351 | 0.9901 | 0.9884 | 0.9893 | 0.9967 | | 0.1249 | 12.0 | 624 | 0.0315 | 0.9909 | 0.9882 | 0.9896 | 0.9969 | | 0.1249 | 13.0 | 676 | 0.0307 | 0.9897 | 0.9888 | 0.9893 | 0.9966 | | 0.1249 | 14.0 | 728 | 0.0268 | 0.9897 | 0.9895 | 0.9896 | 0.9967 | | 0.1249 | 15.0 | 780 | 0.0277 | 0.9897 | 0.9861 | 0.9879 | 0.9962 | | 0.1249 | 16.0 | 832 | 0.0242 | 0.9916 | 0.9893 | 0.9904 | 0.9971 | | 0.1249 | 17.0 | 884 | 0.0214 | 0.9920 | 0.9901 | 0.9910 | 0.9973 | | 0.1249 | 18.0 | 936 | 0.0216 | 0.9920 | 0.9895 | 0.9907 | 0.9972 | | 0.1249 | 19.0 | 988 | 0.0211 | 0.9922 | 0.9899 | 0.9910 | 0.9972 | | 0.0243 | 20.0 | 1040 | 0.0183 | 0.9930 | 0.9912 | 0.9921 | 0.9975 | | 0.0243 | 21.0 | 1092 | 0.0161 | 0.9922 | 0.9907 | 0.9915 | 0.9974 | | 0.0243 | 22.0 | 1144 | 0.0169 | 0.9935 | 0.9914 | 0.9924 | 0.9975 | | 0.0243 | 23.0 | 1196 | 0.0176 | 0.9914 | 0.9897 | 0.9905 | 0.9972 | | 0.0243 | 24.0 | 1248 | 0.0160 | 0.9918 | 0.9905 | 0.9912 | 0.9973 | | 0.0243 | 25.0 | 1300 | 0.0150 | 0.9928 | 0.9918 | 0.9923 | 0.9974 | | 0.0243 | 26.0 | 1352 | 0.0143 | 0.9935 | 0.9918 | 0.9926 | 0.9977 | | 0.0243 | 27.0 | 1404 | 0.0140 | 0.9920 | 0.9918 | 0.9919 | 0.9973 | | 0.0243 | 28.0 | 1456 | 0.0152 | 0.9909 | 0.9905 | 0.9907 | 0.9970 | | 0.0111 | 29.0 | 1508 | 0.0147 | 0.9916 | 0.9907 | 0.9912 | 0.9972 | | 0.0111 | 30.0 | 1560 | 0.0146 | 0.9920 | 0.9912 | 0.9916 | 0.9973 | | 0.0111 | 31.0 | 1612 | 0.0143 | 0.9912 | 0.9905 | 0.9908 | 0.9970 | | 0.0111 | 32.0 | 1664 | 0.0139 | 0.9907 | 0.9905 | 0.9906 | 0.9970 | | 0.0111 | 33.0 | 1716 | 0.0144 | 0.9912 | 0.9912 | 0.9912 | 0.9970 | | 0.0111 | 34.0 | 1768 | 0.0137 | 0.9922 | 0.9916 | 0.9919 | 0.9973 | | 0.0111 | 35.0 | 1820 | 0.0139 | 0.9937 | 0.9926 | 0.9932 | 0.9977 | | 0.0111 | 36.0 | 1872 | 0.0146 | 0.9912 | 0.9914 | 0.9913 | 0.9970 | | 0.0111 | 37.0 | 1924 | 0.0138 | 0.9928 | 0.9920 | 0.9924 | 0.9975 | | 0.0111 | 38.0 | 1976 | 0.0125 | 0.9933 | 0.9928 | 0.9931 | 0.9977 | | 0.0062 | 39.0 | 2028 | 0.0138 | 0.9922 | 0.9914 | 0.9918 | 0.9973 | | 0.0062 | 40.0 | 2080 | 0.0131 | 0.9918 | 0.9910 | 0.9914 | 0.9972 | | 0.0062 | 41.0 | 2132 | 0.0141 | 0.9914 | 0.9912 | 0.9913 | 0.9971 | | 0.0062 | 42.0 | 2184 | 0.0137 | 0.9930 | 0.9922 | 0.9926 | 0.9975 | | 0.0062 | 43.0 | 2236 | 0.0139 | 0.9920 | 0.9910 | 0.9915 | 0.9973 | | 0.0062 | 44.0 | 2288 | 0.0146 | 0.9924 | 0.9920 | 0.9922 | 0.9974 | | 0.0062 | 45.0 | 2340 | 0.0134 | 0.9933 | 0.9924 | 0.9928 | 0.9977 | | 0.0062 | 46.0 | 2392 | 0.0149 | 0.9935 | 0.9924 | 0.9929 | 0.9977 | | 0.0062 | 47.0 | 2444 | 0.0124 | 0.9933 | 0.9926 | 0.9929 | 0.9977 | | 0.0062 | 48.0 | 2496 | 0.0125 | 0.9931 | 0.9924 | 0.9927 | 0.9976 | | 0.0037 | 49.0 | 2548 | 0.0130 | 0.9916 | 0.9910 | 0.9913 | 0.9972 | | 0.0037 | 50.0 | 2600 | 0.0129 | 0.9928 | 0.9924 | 0.9926 | 0.9975 | | 0.0037 | 51.0 | 2652 | 0.0128 | 0.9935 | 0.9926 | 0.9931 | 0.9975 | | 0.0037 | 52.0 | 2704 | 0.0136 | 0.9922 | 0.9918 | 0.9920 | 0.9973 | | 0.0037 | 53.0 | 2756 | 0.0141 | 0.9926 | 0.9916 | 0.9921 | 0.9974 | | 0.0037 | 54.0 | 2808 | 0.0135 | 0.9933 | 0.9926 | 0.9929 | 0.9978 | | 0.0037 | 55.0 | 2860 | 0.0147 | 0.9935 | 0.9922 | 0.9928 | 0.9975 | | 0.0037 | 56.0 | 2912 | 0.0142 | 0.9935 | 0.9926 | 0.9931 | 0.9977 | | 0.0037 | 57.0 | 2964 | 0.0139 | 0.9931 | 0.9926 | 0.9928 | 0.9975 | | 0.0027 | 58.0 | 3016 | 0.0136 | 0.9935 | 0.9928 | 0.9932 | 0.9977 | | 0.0027 | 59.0 | 3068 | 0.0143 | 0.9935 | 0.9926 | 0.9931 | 0.9976 | | 0.0027 | 60.0 | 3120 | 0.0141 | 0.9933 | 0.9926 | 0.9929 | 0.9975 | | 0.0027 | 61.0 | 3172 | 0.0129 | 0.9939 | 0.9931 | 0.9935 | 0.9978 | | 0.0027 | 62.0 | 3224 | 0.0137 | 0.9939 | 0.9931 | 0.9935 | 0.9978 | | 0.0027 | 63.0 | 3276 | 0.0136 | 0.9935 | 0.9924 | 0.9929 | 0.9977 | | 0.0027 | 64.0 | 3328 | 0.0141 | 0.9933 | 0.9926 | 0.9929 | 0.9977 | | 0.0027 | 65.0 | 3380 | 0.0141 | 0.9939 | 0.9931 | 0.9935 | 0.9978 | | 0.0027 | 66.0 | 3432 | 0.0137 | 0.9939 | 0.9928 | 0.9934 | 0.9978 | | 0.0027 | 67.0 | 3484 | 0.0145 | 0.9924 | 0.9916 | 0.992 | 0.9973 | | 0.0019 | 68.0 | 3536 | 0.0150 | 0.9937 | 0.9924 | 0.9931 | 0.9977 | | 0.0019 | 69.0 | 3588 | 0.0152 | 0.9930 | 0.9920 | 0.9925 | 0.9975 | | 0.0019 | 70.0 | 3640 | 0.0149 | 0.9926 | 0.9918 | 0.9922 | 0.9973 | | 0.0019 | 71.0 | 3692 | 0.0143 | 0.9935 | 0.9924 | 0.9929 | 0.9976 | | 0.0019 | 72.0 | 3744 | 0.0152 | 0.9937 | 0.9924 | 0.9931 | 0.9977 | | 0.0019 | 73.0 | 3796 | 0.0149 | 0.9933 | 0.9922 | 0.9927 | 0.9975 | | 0.0019 | 74.0 | 3848 | 0.0158 | 0.9935 | 0.9922 | 0.9928 | 0.9976 | | 0.0019 | 75.0 | 3900 | 0.0153 | 0.9928 | 0.9918 | 0.9923 | 0.9975 | | 0.0019 | 76.0 | 3952 | 0.0154 | 0.9928 | 0.9920 | 0.9924 | 0.9975 | | 0.0015 | 77.0 | 4004 | 0.0145 | 0.9937 | 0.9928 | 0.9933 | 0.9977 | | 0.0015 | 78.0 | 4056 | 0.0158 | 0.9928 | 0.9920 | 0.9924 | 0.9975 | | 0.0015 | 79.0 | 4108 | 0.0161 | 0.9933 | 0.9922 | 0.9927 | 0.9975 | | 0.0015 | 80.0 | 4160 | 0.0155 | 0.9935 | 0.9924 | 0.9929 | 0.9976 | | 0.0015 | 81.0 | 4212 | 0.0157 | 0.9933 | 0.9922 | 0.9927 | 0.9975 | | 0.0015 | 82.0 | 4264 | 0.0155 | 0.9933 | 0.9922 | 0.9927 | 0.9975 | | 0.0015 | 83.0 | 4316 | 0.0144 | 0.9939 | 0.9931 | 0.9935 | 0.9978 | | 0.0015 | 84.0 | 4368 | 0.0142 | 0.9939 | 0.9931 | 0.9935 | 0.9978 | | 0.0015 | 85.0 | 4420 | 0.0145 | 0.9941 | 0.9931 | 0.9936 | 0.9978 | | 0.0015 | 86.0 | 4472 | 0.0151 | 0.9935 | 0.9924 | 0.9929 | 0.9977 | | 0.0013 | 87.0 | 4524 | 0.0143 | 0.9935 | 0.9924 | 0.9929 | 0.9977 | | 0.0013 | 88.0 | 4576 | 0.0145 | 0.9933 | 0.9924 | 0.9928 | 0.9976 | | 0.0013 | 89.0 | 4628 | 0.0142 | 0.9935 | 0.9926 | 0.9931 | 0.9976 | | 0.0013 | 90.0 | 4680 | 0.0142 | 0.9941 | 0.9933 | 0.9937 | 0.9978 | | 0.0013 | 91.0 | 4732 | 0.0145 | 0.9941 | 0.9931 | 0.9936 | 0.9978 | | 0.0013 | 92.0 | 4784 | 0.0146 | 0.9941 | 0.9931 | 0.9936 | 0.9978 | | 0.0013 | 93.0 | 4836 | 0.0146 | 0.9939 | 0.9931 | 0.9935 | 0.9978 | | 0.0013 | 94.0 | 4888 | 0.0147 | 0.9941 | 0.9931 | 0.9936 | 0.9978 | | 0.0013 | 95.0 | 4940 | 0.0148 | 0.9941 | 0.9931 | 0.9936 | 0.9978 | | 0.0013 | 96.0 | 4992 | 0.0147 | 0.9941 | 0.9931 | 0.9936 | 0.9978 | | 0.0011 | 97.0 | 5044 | 0.0147 | 0.9939 | 0.9931 | 0.9935 | 0.9978 | | 0.0011 | 98.0 | 5096 | 0.0147 | 0.9939 | 0.9931 | 0.9935 | 0.9978 | | 0.0011 | 99.0 | 5148 | 0.0147 | 0.9939 | 0.9931 | 0.9935 | 0.9978 | | 0.0011 | 100.0 | 5200 | 0.0147 | 0.9941 | 0.9931 | 0.9936 | 0.9978 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.14.6 - Tokenizers 0.19.1