| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: distilbert/distilbert-base-uncased |
| tags: |
| - generated_from_trainer |
| model-index: |
| - name: distilbert-dapt |
| results: [] |
| datasets: |
| - DerivedFunction/sec-filings-snippets |
| language: |
| - en |
| pipeline_tag: fill-mask |
| --- |
| |
| <!-- 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. --> |
|
|
| # distilbert-dapt |
|
|
| This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on a dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 1.3623 |
|
|
| ## 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: 5e-05 |
| - train_batch_size: 32 |
| - eval_batch_size: 64 |
| - 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: 0.1 |
| - num_epochs: 1 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | |
| |:-------------:|:------:|:----:|:---------------:| |
| | 1.8489 | 0.1195 | 500 | 1.7333 | |
| | 1.6703 | 0.2390 | 1000 | 1.5837 | |
| | 1.5914 | 0.3585 | 1500 | 1.5023 | |
| | 1.5805 | 0.4780 | 2000 | 1.4578 | |
| | 1.5379 | 0.5975 | 2500 | 1.4236 | |
| | 1.4827 | 0.7170 | 3000 | 1.4011 | |
| | 1.4549 | 0.8365 | 3500 | 1.3739 | |
| | 1.4450 | 0.9560 | 4000 | 1.3623 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 5.2.0 |
| - Pytorch 2.10.0+cu128 |
| - Datasets 4.3.0 |
| - Tokenizers 0.22.2 |
