| | --- |
| | license: mit |
| | tags: |
| | - generated_from_trainer |
| | base_model: microsoft/deberta-v3-small |
| | model-index: |
| | - name: nlp-redaction-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. --> |
| |
|
| | # Redaction Classifier: NLP Edition |
| |
|
| | This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on a custom dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.0893 |
| | - Pearson: 0.8273 |
| |
|
| | ## Model description |
| |
|
| | Read more about the process and the code used to train this model on my blog [here](https://mlops.systems). |
| |
|
| | ## 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: 0.0001 |
| | - train_batch_size: 4 |
| | - eval_batch_size: 8 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: cosine |
| | - lr_scheduler_warmup_ratio: 0.1 |
| | - num_epochs: 6 |
| | - mixed_precision_training: Native AMP |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Pearson | |
| | |:-------------:|:-----:|:----:|:---------------:|:-------:| |
| | | 0.2054 | 1.0 | 729 | 0.1382 | 0.6771 | |
| | | 0.1386 | 2.0 | 1458 | 0.1099 | 0.7721 | |
| | | 0.0782 | 3.0 | 2187 | 0.0950 | 0.8083 | |
| | | 0.054 | 4.0 | 2916 | 0.0945 | 0.8185 | |
| | | 0.0319 | 5.0 | 3645 | 0.0880 | 0.8251 | |
| | | 0.0254 | 6.0 | 4374 | 0.0893 | 0.8273 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.19.2 |
| | - Pytorch 1.11.0a0+17540c5 |
| | - Datasets 2.2.2 |
| | - Tokenizers 0.12.1 |
| | |
