---
license: mit
base_model: microsoft/deberta-v3-large
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: metacognitive-cls
  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. -->

# metacognitive-cls

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: 0.1024
- Accuracy: 0.9640
- F1: 0.8326
- Precision: 0.8742
- Recall: 0.7947

## 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: 9.946303722432942e-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 12

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6685        | 1.0   | 76   | 0.6265          | 0.7931   | 0.0543 | 0.0559    | 0.0528 |
| 0.45          | 2.0   | 152  | 0.2973          | 0.8983   | 0.3275 | 0.6410    | 0.2199 |
| 0.2947        | 3.0   | 228  | 0.2671          | 0.9069   | 0.4910 | 0.6385    | 0.3988 |
| 0.2561        | 4.0   | 304  | 0.2246          | 0.9234   | 0.5323 | 0.8516    | 0.3871 |
| 0.2201        | 5.0   | 380  | 0.1926          | 0.9442   | 0.6988 | 0.8909    | 0.5748 |
| 0.1896        | 6.0   | 456  | 0.1704          | 0.9439   | 0.6828 | 0.9385    | 0.5367 |
| 0.1574        | 7.0   | 532  | 0.1468          | 0.9515   | 0.7452 | 0.9110    | 0.6305 |
| 0.1203        | 8.0   | 608  | 0.1213          | 0.9591   | 0.8056 | 0.8653    | 0.7537 |
| 0.0924        | 9.0   | 684  | 0.1119          | 0.9634   | 0.8290 | 0.8734    | 0.7889 |
| 0.0771        | 10.0  | 760  | 0.1073          | 0.9620   | 0.8206 | 0.8767    | 0.7713 |
| 0.067         | 11.0  | 836  | 0.1016          | 0.9657   | 0.8415 | 0.8762    | 0.8094 |
| 0.0609        | 12.0  | 912  | 0.1024          | 0.9640   | 0.8326 | 0.8742    | 0.7947 |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1