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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 |