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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: bert-philosophy-adapted |
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results: [] |
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datasets: |
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- AiresPucrs/stanford-encyclopedia-philosophy |
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language: |
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- en |
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pipeline_tag: fill-mask |
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--- |
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# bert-philosophy-adapted |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.5044 |
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## Model description |
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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. |
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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: 5e-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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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH 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: 500 |
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- num_epochs: 3 |
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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 | |
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|:-------------:|:------:|:-----:|:---------------:| |
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| 2.0568 | 0.1020 | 500 | 1.8821 | |
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| 1.9169 | 0.2039 | 1000 | 1.7939 | |
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| 1.873 | 0.3059 | 1500 | 1.7593 | |
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| 1.8408 | 0.4078 | 2000 | 1.7280 | |
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| 1.8461 | 0.5098 | 2500 | 1.7069 | |
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| 1.8108 | 0.6117 | 3000 | 1.6899 | |
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| 1.7959 | 0.7137 | 3500 | 1.6748 | |
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| 1.7771 | 0.8157 | 4000 | 1.6490 | |
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| 1.7705 | 0.9176 | 4500 | 1.6371 | |
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| 1.725 | 1.0196 | 5000 | 1.6317 | |
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| 1.707 | 1.1215 | 5500 | 1.6279 | |
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| 1.7127 | 1.2235 | 6000 | 1.6100 | |
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| 1.6806 | 1.3254 | 6500 | 1.5978 | |
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| 1.6809 | 1.4274 | 7000 | 1.5920 | |
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| 1.6766 | 1.5294 | 7500 | 1.5831 | |
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| 1.6598 | 1.6313 | 8000 | 1.5748 | |
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| 1.6632 | 1.7333 | 8500 | 1.5646 | |
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| 1.6433 | 1.8352 | 9000 | 1.5554 | |
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| 1.6317 | 1.9372 | 9500 | 1.5552 | |
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| 1.6141 | 2.0392 | 10000 | 1.5404 | |
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| 1.6328 | 2.1411 | 10500 | 1.5393 | |
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| 1.5981 | 2.2431 | 11000 | 1.5330 | |
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| 1.6192 | 2.3450 | 11500 | 1.5260 | |
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| 1.6051 | 2.4470 | 12000 | 1.5198 | |
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| 1.6218 | 2.5489 | 12500 | 1.5162 | |
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| 1.5721 | 2.6509 | 13000 | 1.5079 | |
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| 1.5656 | 2.7529 | 13500 | 1.5109 | |
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| 1.5642 | 2.8548 | 14000 | 1.5077 | |
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| 1.5715 | 2.9568 | 14500 | 1.5106 | |
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### Framework versions |
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- Transformers 4.52.4 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.6.0 |
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- Tokenizers 0.21.1 |
