| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: bert-base-uncased |
| tags: |
| - generated_from_trainer |
| model-index: |
| - name: MsIssuesBERT |
| 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. --> |
|
|
| # MsIssuesBERT |
|
|
| This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: nan |
| - Ethnic Boundaries F1: 0.9313 |
| - Ethnic Boundaries Accuracy: 0.9363 |
| - Economic Inequality F1: 0.8031 |
| - Economic Inequality Accuracy: 0.8123 |
| - Economic Policy Benefits F1: 0.8269 |
| - Economic Policy Benefits Accuracy: 0.8485 |
| - Religion Ethnic Identity F1: 0.8491 |
| - Religion Ethnic Identity Accuracy: 0.8588 |
| - Language Policy F1: 0.6336 |
| - Language Policy Accuracy: 0.7059 |
| - Mother Tongue Education F1: 0.8370 |
| - Mother Tongue Education Accuracy: 0.8889 |
| - Overall F1: 0.8135 |
| - Overall Accuracy: 0.8418 |
|
|
| ## 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: 4.452845612911518e-05 |
| - train_batch_size: 16 |
| - eval_batch_size: 16 |
| - seed: 42 |
| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_steps: 964 |
| - num_epochs: 5 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Ethnic Boundaries F1 | Ethnic Boundaries Accuracy | Economic Inequality F1 | Economic Inequality Accuracy | Economic Policy Benefits F1 | Economic Policy Benefits Accuracy | Religion Ethnic Identity F1 | Religion Ethnic Identity Accuracy | Language Policy F1 | Language Policy Accuracy | Mother Tongue Education F1 | Mother Tongue Education Accuracy | Overall F1 | Overall Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:--------------------:|:--------------------------:|:----------------------:|:----------------------------:|:---------------------------:|:---------------------------------:|:---------------------------:|:---------------------------------:|:------------------:|:------------------------:|:--------------------------:|:--------------------------------:|:----------:|:----------------:| |
| | 0.0242 | 1.0 | 1000 | nan | 0.9199 | 0.9461 | 0.6796 | 0.7771 | 0.7411 | 0.8215 | 0.7662 | 0.8395 | 0.5459 | 0.6765 | 0.6806 | 0.7778 | 0.7222 | 0.8064 | |
| | 0.092 | 2.0 | 2000 | nan | 0.9393 | 0.9444 | 0.7938 | 0.8023 | 0.7996 | 0.8316 | 0.8412 | 0.8569 | 0.6336 | 0.7059 | 0.8370 | 0.8889 | 0.8074 | 0.8383 | |
| | 0.083 | 3.0 | 3000 | nan | 0.9323 | 0.9395 | 0.8053 | 0.8249 | 0.8170 | 0.8519 | 0.8419 | 0.8588 | 0.6071 | 0.7059 | 0.8370 | 0.8889 | 0.8068 | 0.8450 | |
| | 1.6647 | 4.0 | 4000 | nan | 0.9298 | 0.9297 | 0.8046 | 0.8098 | 0.8367 | 0.8586 | 0.8604 | 0.8627 | 0.6573 | 0.7353 | 0.8370 | 0.8889 | 0.8210 | 0.8475 | |
| | 0.0619 | 5.0 | 5000 | nan | 0.9313 | 0.9363 | 0.8031 | 0.8123 | 0.8269 | 0.8485 | 0.8491 | 0.8588 | 0.6336 | 0.7059 | 0.8370 | 0.8889 | 0.8135 | 0.8418 | |
|
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|
| ### Framework versions |
|
|
| - Transformers 4.55.4 |
| - Pytorch 2.8.0+cu126 |
| - Datasets 4.0.0 |
| - Tokenizers 0.21.4 |
|
|
