---
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           |


### Framework versions

- Transformers 4.55.4
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4