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---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-large-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- recall
- precision
- f1
model-index:
- name: bert-question-classifier
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. -->
# bert-question-classifier
This model is a fine-tuned version of [google-bert/bert-large-uncased](https://huggingface.co/google-bert/bert-large-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0746
- Accuracy: 0.9807
- Recall: 0.8925
- Precision: 0.8909
- F1: 0.8917
## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | Precision | F1 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log | 0.1770 | 100 | 5.0786 | 0.8950 | 0.5327 | 0.4279 | 0.4746 |
| No log | 0.3540 | 200 | 4.3278 | 0.9257 | 0.5783 | 0.5838 | 0.5810 |
| No log | 0.5310 | 300 | 3.6620 | 0.9411 | 0.6940 | 0.6612 | 0.6772 |
| No log | 0.7080 | 400 | 3.1590 | 0.9515 | 0.7420 | 0.7211 | 0.7314 |
| 4.1504 | 0.8850 | 500 | 2.5666 | 0.9580 | 0.7770 | 0.7580 | 0.7674 |
| 4.1504 | 1.0619 | 600 | 2.1826 | 0.9638 | 0.7987 | 0.7954 | 0.7970 |
| 4.1504 | 1.2389 | 700 | 1.9754 | 0.9666 | 0.8168 | 0.8094 | 0.8131 |
| 4.1504 | 1.4159 | 800 | 1.8447 | 0.9686 | 0.8372 | 0.8151 | 0.8260 |
| 4.1504 | 1.5929 | 900 | 1.6676 | 0.9706 | 0.8457 | 0.8283 | 0.8369 |
| 2.012 | 1.7699 | 1000 | 1.5743 | 0.9728 | 0.8510 | 0.8450 | 0.8480 |
| 2.012 | 1.9469 | 1100 | 1.4473 | 0.9749 | 0.8641 | 0.8556 | 0.8598 |
| 2.012 | 2.1239 | 1200 | 1.4000 | 0.9749 | 0.8641 | 0.8551 | 0.8596 |
| 2.012 | 2.3009 | 1300 | 1.3287 | 0.9772 | 0.8764 | 0.8688 | 0.8726 |
| 2.012 | 2.4779 | 1400 | 1.2995 | 0.9770 | 0.8773 | 0.8659 | 0.8715 |
| 1.3018 | 2.6549 | 1500 | 1.2397 | 0.9778 | 0.8793 | 0.8724 | 0.8759 |
| 1.3018 | 2.8319 | 1600 | 1.2059 | 0.9786 | 0.8857 | 0.8753 | 0.8805 |
| 1.3018 | 3.0088 | 1700 | 1.1763 | 0.9790 | 0.8857 | 0.8798 | 0.8828 |
| 1.3018 | 3.1858 | 1800 | 1.1744 | 0.9786 | 0.8816 | 0.8788 | 0.8802 |
| 1.3018 | 3.3628 | 1900 | 1.1356 | 0.9793 | 0.8869 | 0.8818 | 0.8843 |
| 0.9668 | 3.5398 | 2000 | 1.1365 | 0.9791 | 0.8857 | 0.8806 | 0.8832 |
| 0.9668 | 3.7168 | 2100 | 1.1084 | 0.9796 | 0.8872 | 0.8838 | 0.8855 |
| 0.9668 | 3.8938 | 2200 | 1.0939 | 0.9800 | 0.8892 | 0.8864 | 0.8878 |
| 0.9668 | 4.0708 | 2300 | 1.0974 | 0.9796 | 0.8881 | 0.8834 | 0.8857 |
| 0.9668 | 4.2478 | 2400 | 1.0786 | 0.9802 | 0.8916 | 0.8864 | 0.8890 |
| 0.7915 | 4.4248 | 2500 | 1.0766 | 0.9803 | 0.8910 | 0.8881 | 0.8896 |
| 0.7915 | 4.6018 | 2600 | 1.0746 | 0.9807 | 0.8925 | 0.8909 | 0.8917 |
| 0.7915 | 4.7788 | 2700 | 1.0686 | 0.9803 | 0.8910 | 0.8887 | 0.8898 |
| 0.7915 | 4.9558 | 2800 | 1.0637 | 0.9802 | 0.8907 | 0.8873 | 0.8890 |
### Framework versions
- Transformers 4.48.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
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