---
library_name: transformers
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta_mcq
  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_mcq

This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0868
- Accuracy: 0.9962
- F1 Macro: 0.9960
- Map At 3: 0.9981

## 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: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 6

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1 Macro | Map At 3 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|:--------:|
| 1.6085        | 0.9840 | 46   | 1.5929          | 0.6061   | 0.5985   | 0.7506   |
| 1.3478        | 1.9893 | 93   | 0.7228          | 0.9242   | 0.9211   | 0.9558   |
| 0.4559        | 2.9947 | 140  | 0.2588          | 0.9848   | 0.9839   | 0.9924   |
| 0.3132        | 4.0    | 187  | 0.1472          | 0.9924   | 0.9920   | 0.9962   |
| 0.2326        | 4.9840 | 233  | 0.0957          | 0.9962   | 0.9960   | 0.9981   |
| 0.201         | 5.9037 | 276  | 0.0868          | 0.9962   | 0.9960   | 0.9981   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.13.0+cu130
- Datasets 4.8.5
- Tokenizers 0.19.1