---
library_name: transformers
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: deberta-4-disfluency
  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. -->

# deberta-4-disfluency

This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0093
- Precision: 0.9149
- Recall: 0.9306
- F1: 0.9226
- Accuracy: 0.9841
- Disfluent Precision: 0.9533
- Disfluent Recall: 0.9667
- Disfluent F1: 0.9599
- False Positive Rate: 0.0115

## 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: 2e-05
- train_batch_size: 64
- eval_batch_size: 192
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Accuracy | Disfluent Precision | Disfluent Recall | Disfluent F1 | False Positive Rate |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:-------------------:|:----------------:|:------------:|:-------------------:|
| 0.0079        | 0.1821 | 250  | 0.0090          | 0.9100    | 0.9259 | 0.9179 | 0.9833   | 0.9522              | 0.9633           | 0.9577       | 0.0117              |
| 0.0077        | 0.3642 | 500  | 0.0098          | 0.9118    | 0.9296 | 0.9206 | 0.9840   | 0.9595              | 0.9592           | 0.9594       | 0.0098              |
| 0.0059        | 0.5462 | 750  | 0.0146          | 0.9168    | 0.9287 | 0.9227 | 0.9836   | 0.9654              | 0.9508           | 0.9581       | 0.0083              |
| 0.0058        | 0.7283 | 1000 | 0.0109          | 0.9157    | 0.9242 | 0.9199 | 0.9837   | 0.9549              | 0.9623           | 0.9586       | 0.0110              |
| 0.0055        | 0.9104 | 1250 | 0.0116          | 0.9131    | 0.9279 | 0.9205 | 0.9834   | 0.9530              | 0.9632           | 0.9581       | 0.0115              |
| 0.0072        | 1.0925 | 1500 | 0.0093          | 0.8936    | 0.9201 | 0.9067 | 0.9815   | 0.9396              | 0.9679           | 0.9535       | 0.0150              |
| 0.0062        | 1.2746 | 1750 | 0.0103          | 0.9085    | 0.9226 | 0.9155 | 0.9822   | 0.9455              | 0.9652           | 0.9553       | 0.0134              |
| 0.0059        | 1.4567 | 2000 | 0.0088          | 0.9079    | 0.9225 | 0.9151 | 0.9816   | 0.9371              | 0.9715           | 0.9540       | 0.0157              |
| 0.0062        | 1.6387 | 2250 | 0.0093          | 0.9149    | 0.9306 | 0.9226 | 0.9841   | 0.9533              | 0.9667           | 0.9599       | 0.0115              |


### Framework versions

- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1