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ijelid-bert-base-multilingual

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+ ---
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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: ijelid-bert-base-multilingual
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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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+ # ijelid-bert-base-multilingual
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+
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+ This model was trained from scratch on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.3553
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+ - Precision: 0.9189
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+ - Recall: 0.9188
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+ - F1: 0.9187
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+ - Accuracy: 0.9451
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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: 2e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 16
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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: 20
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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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+ | No log | 1.0 | 386 | 0.2340 | 0.8956 | 0.8507 | 0.8715 | 0.9239 |
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+ | 0.3379 | 2.0 | 772 | 0.2101 | 0.9057 | 0.8904 | 0.8962 | 0.9342 |
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+ | 0.1603 | 3.0 | 1158 | 0.2231 | 0.9252 | 0.8896 | 0.9065 | 0.9367 |
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+ | 0.1079 | 4.0 | 1544 | 0.2013 | 0.9272 | 0.8902 | 0.9070 | 0.9420 |
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+ | 0.1079 | 5.0 | 1930 | 0.2179 | 0.9031 | 0.9179 | 0.9103 | 0.9425 |
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+ | 0.0701 | 6.0 | 2316 | 0.2330 | 0.9075 | 0.9165 | 0.9114 | 0.9435 |
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+ | 0.051 | 7.0 | 2702 | 0.2433 | 0.9117 | 0.9190 | 0.9150 | 0.9432 |
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+ | 0.0384 | 8.0 | 3088 | 0.2545 | 0.9001 | 0.9167 | 0.9078 | 0.9439 |
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+ | 0.0384 | 9.0 | 3474 | 0.2629 | 0.9164 | 0.9159 | 0.9158 | 0.9444 |
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+ | 0.0293 | 10.0 | 3860 | 0.2881 | 0.9263 | 0.9096 | 0.9178 | 0.9427 |
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+ | 0.022 | 11.0 | 4246 | 0.2882 | 0.9167 | 0.9222 | 0.9191 | 0.9450 |
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+ | 0.0171 | 12.0 | 4632 | 0.3028 | 0.9203 | 0.9152 | 0.9177 | 0.9447 |
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+ | 0.0143 | 13.0 | 5018 | 0.3236 | 0.9155 | 0.9167 | 0.9158 | 0.9440 |
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+ | 0.0143 | 14.0 | 5404 | 0.3301 | 0.9237 | 0.9163 | 0.9199 | 0.9444 |
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+ | 0.0109 | 15.0 | 5790 | 0.3290 | 0.9187 | 0.9154 | 0.9169 | 0.9442 |
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+ | 0.0092 | 16.0 | 6176 | 0.3308 | 0.9213 | 0.9178 | 0.9194 | 0.9448 |
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+ | 0.0075 | 17.0 | 6562 | 0.3501 | 0.9273 | 0.9142 | 0.9206 | 0.9445 |
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+ | 0.0075 | 18.0 | 6948 | 0.3520 | 0.9200 | 0.9184 | 0.9190 | 0.9447 |
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+ | 0.0062 | 19.0 | 7334 | 0.3524 | 0.9238 | 0.9183 | 0.9210 | 0.9458 |
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+ | 0.0051 | 20.0 | 7720 | 0.3553 | 0.9189 | 0.9188 | 0.9187 | 0.9451 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.21.2
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+ - Pytorch 1.7.1
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+ - Datasets 2.5.1
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+ - Tokenizers 0.12.1