| --- |
| license: apache-2.0 |
| base_model: bert-base-multilingual-uncased |
| tags: |
| - generated_from_trainer |
| metrics: |
| - recall |
| - accuracy |
| model-index: |
| - name: MultiBERTBestModelOct11 |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # multibert0510_lrate7.5b16 |
| |
| This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.5930 |
| - Precisions: 0.8750 |
| - Recall: 0.8217 |
| - F-measure: 0.8450 |
| - Accuracy: 0.9133 |
| |
| ## 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: 7.5e-05 |
| - train_batch_size: 16 |
| - eval_batch_size: 16 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 14 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Precisions | Recall | F-measure | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:----------:|:------:|:---------:|:--------:| |
| | 0.6022 | 1.0 | 236 | 0.4256 | 0.8484 | 0.6548 | 0.6844 | 0.8642 | |
| | 0.3396 | 2.0 | 472 | 0.3851 | 0.8046 | 0.7225 | 0.7312 | 0.8773 | |
| | 0.2116 | 3.0 | 708 | 0.3670 | 0.8311 | 0.7347 | 0.7560 | 0.8947 | |
| | 0.148 | 4.0 | 944 | 0.4016 | 0.8827 | 0.7716 | 0.8081 | 0.9021 | |
| | 0.0959 | 5.0 | 1180 | 0.4409 | 0.8338 | 0.8054 | 0.8166 | 0.8998 | |
| | 0.0809 | 6.0 | 1416 | 0.4964 | 0.8678 | 0.7356 | 0.7799 | 0.8980 | |
| | 0.056 | 7.0 | 1652 | 0.4894 | 0.8451 | 0.7520 | 0.7855 | 0.8931 | |
| | 0.038 | 8.0 | 1888 | 0.5008 | 0.8697 | 0.8024 | 0.8301 | 0.9104 | |
| | 0.031 | 9.0 | 2124 | 0.4813 | 0.8561 | 0.8172 | 0.8335 | 0.9122 | |
| | 0.02 | 10.0 | 2360 | 0.5857 | 0.8831 | 0.7946 | 0.8305 | 0.9115 | |
| | 0.0129 | 11.0 | 2596 | 0.5622 | 0.8667 | 0.8039 | 0.8308 | 0.9098 | |
| | 0.0113 | 12.0 | 2832 | 0.5861 | 0.8746 | 0.8015 | 0.8324 | 0.9104 | |
| | 0.0065 | 13.0 | 3068 | 0.5964 | 0.8752 | 0.8204 | 0.8443 | 0.9128 | |
| | 0.004 | 14.0 | 3304 | 0.5930 | 0.8750 | 0.8217 | 0.8450 | 0.9133 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.34.0 |
| - Pytorch 2.0.1+cu118 |
| - Datasets 2.14.5 |
| - Tokenizers 0.14.0 |
| |
