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--- |
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license: apache-2.0 |
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base_model: google-bert/bert-base-multilingual-cased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: results |
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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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# results |
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This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co./google-bert/bert-base-multilingual-cased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4003 |
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- Accuracy: 0.8589 |
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- F1: 0.7308 |
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- Precision: 0.7238 |
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- Recall: 0.7379 |
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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: 5e-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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- lr_scheduler_warmup_steps: 100 |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| No log | 0.2623 | 16 | 0.6597 | 0.7406 | 0.0 | 0.0 | 0.0 | |
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| No log | 0.5246 | 32 | 0.5547 | 0.7406 | 0.0 | 0.0 | 0.0 | |
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| No log | 0.7869 | 48 | 0.5144 | 0.7406 | 0.0 | 0.0 | 0.0 | |
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| No log | 1.0492 | 64 | 0.4658 | 0.8237 | 0.5205 | 0.8837 | 0.3689 | |
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| No log | 1.3115 | 80 | 0.4164 | 0.8338 | 0.7 | 0.6581 | 0.7476 | |
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| No log | 1.5738 | 96 | 0.3812 | 0.8212 | 0.6872 | 0.6290 | 0.7573 | |
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| No log | 1.8361 | 112 | 0.3799 | 0.8564 | 0.6705 | 0.8286 | 0.5631 | |
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| No log | 2.0984 | 128 | 0.3736 | 0.8111 | 0.6725 | 0.6111 | 0.7476 | |
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| No log | 2.3607 | 144 | 0.3726 | 0.8564 | 0.7047 | 0.7556 | 0.6602 | |
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| No log | 2.6230 | 160 | 0.4651 | 0.7456 | 0.6456 | 0.5055 | 0.8932 | |
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| No log | 2.8852 | 176 | 0.3592 | 0.8413 | 0.7070 | 0.6786 | 0.7379 | |
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| No log | 3.1475 | 192 | 0.3633 | 0.8514 | 0.7035 | 0.7292 | 0.6796 | |
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| No log | 3.4098 | 208 | 0.4381 | 0.8086 | 0.6984 | 0.5906 | 0.8544 | |
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| No log | 3.6721 | 224 | 0.4114 | 0.8338 | 0.7080 | 0.6504 | 0.7767 | |
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| No log | 3.9344 | 240 | 0.4588 | 0.8186 | 0.7025 | 0.6115 | 0.8252 | |
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| No log | 4.1967 | 256 | 0.3795 | 0.8615 | 0.7291 | 0.74 | 0.7184 | |
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| No log | 4.4590 | 272 | 0.4418 | 0.8262 | 0.7113 | 0.625 | 0.8252 | |
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| No log | 4.7213 | 288 | 0.3962 | 0.8489 | 0.7170 | 0.6972 | 0.7379 | |
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| No log | 4.9836 | 304 | 0.4003 | 0.8589 | 0.7308 | 0.7238 | 0.7379 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Tokenizers 0.19.1 |
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