---
library_name: transformers
license: mit
base_model: microsoft/deberta-v3-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-misconception-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. -->

# deberta-misconception-classifier

This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2595
- Macro F1: 0.6012
- Weighted F1: 0.7862
- Accuracy: 0.7823
- Map@3: 0.8846

## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- 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
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Macro F1 | Weighted F1 | Accuracy | Map@3  |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-----------:|:--------:|:------:|
| 1.3548        | 0.2422 | 500  | 1.0357          | 0.2067   | 0.4193      | 0.4221   | 0.5941 |
| 0.9062        | 0.4845 | 1000 | 0.7145          | 0.3536   | 0.6222      | 0.6183   | 0.7672 |
| 0.5924        | 0.7267 | 1500 | 0.4780          | 0.4251   | 0.7250      | 0.7368   | 0.8460 |
| 0.4113        | 0.9690 | 2000 | 0.4354          | 0.4210   | 0.7139      | 0.7354   | 0.8430 |
| 0.2906        | 1.2112 | 2500 | 0.3885          | 0.4757   | 0.7373      | 0.7559   | 0.8635 |
| 0.3248        | 1.4535 | 3000 | 0.3100          | 0.5215   | 0.7591      | 0.7589   | 0.8651 |
| 0.264         | 1.6957 | 3500 | 0.3245          | 0.5371   | 0.7838      | 0.7864   | 0.8852 |
| 0.3461        | 1.9380 | 4000 | 0.2863          | 0.5582   | 0.8036      | 0.8136   | 0.8988 |
| 0.202         | 2.1802 | 4500 | 0.2697          | 0.5758   | 0.8058      | 0.8147   | 0.9013 |
| 0.1641        | 2.4225 | 5000 | 0.2837          | 0.6015   | 0.8224      | 0.8245   | 0.9062 |
| 0.1642        | 2.6647 | 5500 | 0.2991          | 0.5559   | 0.8113      | 0.8139   | 0.9009 |
| 0.1857        | 2.9070 | 6000 | 0.2518          | 0.5931   | 0.8051      | 0.8109   | 0.8995 |
| 0.1322        | 3.1492 | 6500 | 0.2595          | 0.6012   | 0.7862      | 0.7823   | 0.8846 |


### Framework versions

- Transformers 4.53.3
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.2