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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
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