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library_name: transformers
license: mit
base_model: microsoft/deberta-v3-small
tags:
- generated_from_trainer
metrics:
- f1
- recall
- precision
model-index:
- name: PII-Binary-Filter-Extreme-Recall-Fix
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. -->
# PII-Binary-Filter-Extreme-Recall-Fix
This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1361
- F1: 0.9852
- Recall: 0.9879
- Precision: 0.9826
- Trash Caught: 0.6422
## 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: 3e-05
- train_batch_size: 16
- 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
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Recall | Precision | Trash Caught |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:---------:|:------------:|
| No log | 1.0 | 499 | 0.1403 | 0.9785 | 0.9953 | 0.9622 | 0.2018 |
| 0.3066 | 2.0 | 998 | 0.1123 | 0.9832 | 0.9908 | 0.9757 | 0.4954 |
| 0.179 | 3.0 | 1497 | 0.1188 | 0.9853 | 0.9910 | 0.9796 | 0.5780 |
| 0.1209 | 4.0 | 1996 | 0.1293 | 0.9857 | 0.9921 | 0.9794 | 0.5734 |
| 0.0834 | 5.0 | 2495 | 0.1361 | 0.9852 | 0.9879 | 0.9826 | 0.6422 |
### Framework versions
- Transformers 4.56.0
- Pytorch 2.8.0+cu129
- Datasets 4.8.2
- Tokenizers 0.22.0
|