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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: bert-base-uncased |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: MsIssuesBERT |
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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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# MsIssuesBERT |
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: nan |
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- Ethnic Boundaries F1: 0.9313 |
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- Ethnic Boundaries Accuracy: 0.9363 |
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- Economic Inequality F1: 0.8031 |
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- Economic Inequality Accuracy: 0.8123 |
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- Economic Policy Benefits F1: 0.8269 |
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- Economic Policy Benefits Accuracy: 0.8485 |
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- Religion Ethnic Identity F1: 0.8491 |
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- Religion Ethnic Identity Accuracy: 0.8588 |
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- Language Policy F1: 0.6336 |
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- Language Policy Accuracy: 0.7059 |
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- Mother Tongue Education F1: 0.8370 |
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- Mother Tongue Education Accuracy: 0.8889 |
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- Overall F1: 0.8135 |
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- Overall Accuracy: 0.8418 |
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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: 4.452845612911518e-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: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 964 |
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- num_epochs: 5 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| 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 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------------------:|:--------------------------:|:----------------------:|:----------------------------:|:---------------------------:|:---------------------------------:|:---------------------------:|:---------------------------------:|:------------------:|:------------------------:|:--------------------------:|:--------------------------------:|:----------:|:----------------:| |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 |
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- Transformers 4.55.4 |
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- Pytorch 2.8.0+cu126 |
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- Datasets 4.0.0 |
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- Tokenizers 0.21.4 |
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