---
library_name: peft
license: mit
base_model: microsoft/deberta-v3-small
tags:
- base_model:adapter:microsoft/deberta-v3-small
- lora
- transformers
metrics:
- accuracy
- recall
- precision
- f1
model-index:
- name: medical_teacher_update_reomved_v0
  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. -->

# medical_teacher_update_reomved_v0

This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0290
- Accuracy: 0.9967
- Recall: 0.9967
- Precision: 0.9967
- F1: 0.9967

## 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: 0.0002
- train_batch_size: 8
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- 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: linear
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 2
- label_smoothing_factor: 0.1

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Recall | Precision | F1     |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.0055        | 0.4009 | 375  | 0.0742          | 0.9893   | 0.9893 | 0.9896    | 0.9893 |
| 0.0026        | 0.8017 | 750  | 0.0705          | 0.9877   | 0.9877 | 0.9878    | 0.9877 |
| 0.0027        | 1.2020 | 1125 | 0.0316          | 0.9967   | 0.9967 | 0.9967    | 0.9967 |
| 0.0026        | 1.6029 | 1500 | 0.0290          | 0.9967   | 0.9967 | 0.9967    | 0.9967 |


### Framework versions

- PEFT 0.18.1
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.8.3
- Tokenizers 0.22.2