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---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: bert-philosophy-adapted
results: []
datasets:
- AiresPucrs/stanford-encyclopedia-philosophy
language:
- en
pipeline_tag: fill-mask
---
# bert-philosophy-adapted
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the [Standford Encylcopedia of Philosophy](https://huggingface.co/datasets/AiresPucrs/stanford-encyclopedia-philosophy) dataset, using masked language modeling.
It achieves the following results on the evaluation set:
- Loss: 1.5044
## Model description
This model was trained with the intention of creating a BERT encoder model for philosophical terminology, and further training on downstream tasks such as school of philosophy text classification.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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_steps: 500
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:-----:|:---------------:|
| 2.0568 | 0.1020 | 500 | 1.8821 |
| 1.9169 | 0.2039 | 1000 | 1.7939 |
| 1.873 | 0.3059 | 1500 | 1.7593 |
| 1.8408 | 0.4078 | 2000 | 1.7280 |
| 1.8461 | 0.5098 | 2500 | 1.7069 |
| 1.8108 | 0.6117 | 3000 | 1.6899 |
| 1.7959 | 0.7137 | 3500 | 1.6748 |
| 1.7771 | 0.8157 | 4000 | 1.6490 |
| 1.7705 | 0.9176 | 4500 | 1.6371 |
| 1.725 | 1.0196 | 5000 | 1.6317 |
| 1.707 | 1.1215 | 5500 | 1.6279 |
| 1.7127 | 1.2235 | 6000 | 1.6100 |
| 1.6806 | 1.3254 | 6500 | 1.5978 |
| 1.6809 | 1.4274 | 7000 | 1.5920 |
| 1.6766 | 1.5294 | 7500 | 1.5831 |
| 1.6598 | 1.6313 | 8000 | 1.5748 |
| 1.6632 | 1.7333 | 8500 | 1.5646 |
| 1.6433 | 1.8352 | 9000 | 1.5554 |
| 1.6317 | 1.9372 | 9500 | 1.5552 |
| 1.6141 | 2.0392 | 10000 | 1.5404 |
| 1.6328 | 2.1411 | 10500 | 1.5393 |
| 1.5981 | 2.2431 | 11000 | 1.5330 |
| 1.6192 | 2.3450 | 11500 | 1.5260 |
| 1.6051 | 2.4470 | 12000 | 1.5198 |
| 1.6218 | 2.5489 | 12500 | 1.5162 |
| 1.5721 | 2.6509 | 13000 | 1.5079 |
| 1.5656 | 2.7529 | 13500 | 1.5109 |
| 1.5642 | 2.8548 | 14000 | 1.5077 |
| 1.5715 | 2.9568 | 14500 | 1.5106 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1 |