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mBERT-naamapdam-fine-tuned

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+ ---
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+ license: apache-2.0
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - precision
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+ - recall
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+ - f1
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+ - accuracy
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+ model-index:
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+ - name: mBERT-naamapdam-fine-tuned
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # mBERT-naamapdam-fine-tuned
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+
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+ This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.4625
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+ - Precision: 0.8060
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+ - Recall: 0.8246
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+ - F1: 0.8152
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+ - Accuracy: 0.9173
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 128
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+ - eval_batch_size: 256
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 15
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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+ |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 0.3625 | 0.26 | 1000 | 0.3300 | 0.7651 | 0.7809 | 0.7729 | 0.8964 |
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+ | 0.3099 | 0.51 | 2000 | 0.3070 | 0.7708 | 0.8041 | 0.7871 | 0.9002 |
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+ | 0.2954 | 0.77 | 3000 | 0.2962 | 0.7793 | 0.8036 | 0.7913 | 0.9041 |
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+ | 0.283 | 1.03 | 4000 | 0.2958 | 0.7843 | 0.8153 | 0.7995 | 0.9066 |
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+ | 0.265 | 1.29 | 5000 | 0.2873 | 0.7930 | 0.8065 | 0.7997 | 0.9069 |
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+ | 0.2613 | 1.54 | 6000 | 0.2838 | 0.7789 | 0.8289 | 0.8031 | 0.9092 |
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+ | 0.2635 | 1.8 | 7000 | 0.2790 | 0.7902 | 0.8252 | 0.8073 | 0.9088 |
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+ | 0.2574 | 2.06 | 8000 | 0.2946 | 0.7887 | 0.8345 | 0.8110 | 0.9098 |
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+ | 0.2355 | 2.31 | 9000 | 0.2859 | 0.7975 | 0.8152 | 0.8063 | 0.9105 |
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+ | 0.2361 | 2.57 | 10000 | 0.2806 | 0.7883 | 0.8313 | 0.8092 | 0.9104 |
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+ | 0.2361 | 2.83 | 11000 | 0.2805 | 0.7931 | 0.8279 | 0.8101 | 0.9123 |
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+ | 0.2268 | 3.08 | 12000 | 0.2934 | 0.7959 | 0.8323 | 0.8137 | 0.9130 |
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+ | 0.2106 | 3.34 | 13000 | 0.2862 | 0.7934 | 0.8311 | 0.8118 | 0.9121 |
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+ | 0.2106 | 3.6 | 14000 | 0.2876 | 0.8009 | 0.8332 | 0.8167 | 0.9143 |
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+ | 0.2131 | 3.86 | 15000 | 0.2777 | 0.8015 | 0.8242 | 0.8127 | 0.9123 |
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+ | 0.1993 | 4.11 | 16000 | 0.2999 | 0.7920 | 0.8311 | 0.8111 | 0.9113 |
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+ | 0.1872 | 4.37 | 17000 | 0.2984 | 0.8003 | 0.8365 | 0.8180 | 0.9143 |
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+ | 0.1861 | 4.63 | 18000 | 0.2894 | 0.7976 | 0.8321 | 0.8145 | 0.9151 |
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+ | 0.1916 | 4.88 | 19000 | 0.2909 | 0.7958 | 0.8300 | 0.8125 | 0.9143 |
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+ | 0.1745 | 5.14 | 20000 | 0.3075 | 0.7906 | 0.8386 | 0.8139 | 0.9136 |
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+ | 0.1649 | 5.4 | 21000 | 0.2986 | 0.8055 | 0.8199 | 0.8127 | 0.9147 |
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+ | 0.1678 | 5.66 | 22000 | 0.3043 | 0.7988 | 0.8303 | 0.8142 | 0.9147 |
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+ | 0.1688 | 5.91 | 23000 | 0.2950 | 0.8026 | 0.8269 | 0.8146 | 0.9155 |
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+ | 0.153 | 6.17 | 24000 | 0.3231 | 0.7995 | 0.8305 | 0.8147 | 0.9150 |
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+ | 0.1468 | 6.43 | 25000 | 0.3145 | 0.7954 | 0.8326 | 0.8136 | 0.9156 |
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+ | 0.1478 | 6.68 | 26000 | 0.3222 | 0.8034 | 0.8307 | 0.8168 | 0.9160 |
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+ | 0.1489 | 6.94 | 27000 | 0.3184 | 0.8019 | 0.8318 | 0.8166 | 0.9161 |
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+ | 0.1311 | 7.2 | 28000 | 0.3336 | 0.8022 | 0.8278 | 0.8148 | 0.9168 |
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+ | 0.1298 | 7.46 | 29000 | 0.3430 | 0.8050 | 0.8281 | 0.8164 | 0.9164 |
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+ | 0.1319 | 7.71 | 30000 | 0.3374 | 0.8005 | 0.8257 | 0.8129 | 0.9152 |
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+ | 0.1312 | 7.97 | 31000 | 0.3320 | 0.8019 | 0.8353 | 0.8183 | 0.9173 |
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+ | 0.1144 | 8.23 | 32000 | 0.3539 | 0.8007 | 0.8309 | 0.8155 | 0.9160 |
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+ | 0.1132 | 8.48 | 33000 | 0.3581 | 0.7940 | 0.8376 | 0.8152 | 0.9158 |
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+ | 0.1159 | 8.74 | 34000 | 0.3566 | 0.8032 | 0.8355 | 0.8191 | 0.9182 |
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+ | 0.117 | 9.0 | 35000 | 0.3384 | 0.8113 | 0.8205 | 0.8159 | 0.9166 |
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+ | 0.0996 | 9.25 | 36000 | 0.3637 | 0.8060 | 0.8256 | 0.8156 | 0.9166 |
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+ | 0.1004 | 9.51 | 37000 | 0.3687 | 0.8043 | 0.8147 | 0.8095 | 0.9152 |
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+ | 0.1015 | 9.77 | 38000 | 0.3715 | 0.8017 | 0.8359 | 0.8185 | 0.9173 |
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+ | 0.1001 | 10.03 | 39000 | 0.3826 | 0.8047 | 0.8288 | 0.8166 | 0.9174 |
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+ | 0.0874 | 10.28 | 40000 | 0.3857 | 0.8087 | 0.8231 | 0.8158 | 0.9168 |
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+ | 0.0892 | 10.54 | 41000 | 0.3817 | 0.8069 | 0.8221 | 0.8145 | 0.9165 |
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+ | 0.0895 | 10.8 | 42000 | 0.3800 | 0.8107 | 0.8291 | 0.8198 | 0.9183 |
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+ | 0.0868 | 11.05 | 43000 | 0.4099 | 0.8032 | 0.8297 | 0.8162 | 0.9177 |
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+ | 0.0777 | 11.31 | 44000 | 0.4099 | 0.8059 | 0.8255 | 0.8156 | 0.9170 |
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+ | 0.0781 | 11.57 | 45000 | 0.4077 | 0.8044 | 0.8335 | 0.8187 | 0.9186 |
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+ | 0.0779 | 11.83 | 46000 | 0.4172 | 0.8050 | 0.8243 | 0.8145 | 0.9161 |
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+ | 0.0759 | 12.08 | 47000 | 0.4230 | 0.8034 | 0.8244 | 0.8138 | 0.9158 |
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+ | 0.0691 | 12.34 | 48000 | 0.4286 | 0.8048 | 0.8221 | 0.8134 | 0.9162 |
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+ | 0.0676 | 12.6 | 49000 | 0.4251 | 0.8091 | 0.8287 | 0.8188 | 0.9185 |
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+ | 0.0695 | 12.85 | 50000 | 0.4289 | 0.8043 | 0.8284 | 0.8161 | 0.9168 |
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+ | 0.0663 | 13.11 | 51000 | 0.4431 | 0.8060 | 0.8246 | 0.8152 | 0.9168 |
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+ | 0.0618 | 13.37 | 52000 | 0.4484 | 0.8054 | 0.8214 | 0.8133 | 0.9162 |
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+ | 0.0614 | 13.62 | 53000 | 0.4421 | 0.8044 | 0.8230 | 0.8136 | 0.9166 |
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+ | 0.0611 | 13.88 | 54000 | 0.4468 | 0.8066 | 0.8231 | 0.8148 | 0.9166 |
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+ | 0.0582 | 14.14 | 55000 | 0.4606 | 0.8055 | 0.8244 | 0.8148 | 0.9173 |
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+ | 0.0552 | 14.4 | 56000 | 0.4642 | 0.8055 | 0.8274 | 0.8163 | 0.9175 |
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+ | 0.0553 | 14.65 | 57000 | 0.4633 | 0.8083 | 0.8248 | 0.8165 | 0.9175 |
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+ | 0.0556 | 14.91 | 58000 | 0.4625 | 0.8060 | 0.8246 | 0.8152 | 0.9173 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.27.4
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+ - Pytorch 2.0.0+cu117
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+ - Datasets 2.11.0
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+ - Tokenizers 0.13.3