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--- |
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license: apache-2.0 |
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base_model: bert-base-multilingual-uncased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- recall |
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- accuracy |
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model-index: |
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- name: multibert_seed34_1611 |
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results: [] |
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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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# multibert_seed34_1611 |
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This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co./bert-base-multilingual-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4810 |
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- Precisions: 0.8743 |
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- Recall: 0.8016 |
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- F-measure: 0.8318 |
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- Accuracy: 0.9364 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 7.5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 34 |
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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: 14 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precisions | Recall | F-measure | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:----------:|:------:|:---------:|:--------:| |
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| 0.4954 | 1.0 | 236 | 0.2579 | 0.8908 | 0.7174 | 0.7485 | 0.9181 | |
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| 0.2427 | 2.0 | 472 | 0.2589 | 0.8472 | 0.7340 | 0.7497 | 0.9209 | |
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| 0.1427 | 3.0 | 708 | 0.2844 | 0.8461 | 0.7830 | 0.8096 | 0.9325 | |
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| 0.0916 | 4.0 | 944 | 0.3453 | 0.8497 | 0.7804 | 0.8122 | 0.9306 | |
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| 0.0616 | 5.0 | 1180 | 0.3281 | 0.8500 | 0.7936 | 0.8160 | 0.9303 | |
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| 0.0414 | 6.0 | 1416 | 0.3859 | 0.8494 | 0.7930 | 0.8167 | 0.9337 | |
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| 0.0272 | 7.0 | 1652 | 0.3863 | 0.8572 | 0.7894 | 0.8167 | 0.9323 | |
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| 0.0207 | 8.0 | 1888 | 0.3998 | 0.8525 | 0.7938 | 0.8195 | 0.9337 | |
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| 0.0117 | 9.0 | 2124 | 0.4348 | 0.8555 | 0.7983 | 0.8228 | 0.9330 | |
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| 0.0089 | 10.0 | 2360 | 0.4858 | 0.8699 | 0.7708 | 0.7996 | 0.9294 | |
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| 0.0054 | 11.0 | 2596 | 0.4676 | 0.8559 | 0.7959 | 0.8197 | 0.9344 | |
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| 0.0036 | 12.0 | 2832 | 0.4582 | 0.8665 | 0.8038 | 0.8291 | 0.9364 | |
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| 0.0025 | 13.0 | 3068 | 0.4810 | 0.8743 | 0.8016 | 0.8318 | 0.9364 | |
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| 0.0018 | 14.0 | 3304 | 0.4801 | 0.8685 | 0.8036 | 0.8309 | 0.9366 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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