---
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: 0.1544
- Accuracy: 0.9477
- F1: 0.8277
- Precision: 0.8781
- Recall: 0.7827

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.692         | 1.0   | 61   | 0.6603          | 0.6342   | 0.2154 | 0.1642    | 0.3131 |
| 0.5269        | 2.0   | 122  | 0.3794          | 0.8719   | 0.5059 | 0.6632    | 0.4089 |
| 0.3652        | 3.0   | 183  | 0.3409          | 0.8770   | 0.4737 | 0.7552    | 0.3450 |
| 0.3282        | 4.0   | 244  | 0.3081          | 0.8940   | 0.5586 | 0.8397    | 0.4185 |
| 0.2936        | 5.0   | 305  | 0.2756          | 0.9022   | 0.5892 | 0.9013    | 0.4377 |
| 0.2529        | 6.0   | 366  | 0.2550          | 0.9129   | 0.6805 | 0.8265    | 0.5783 |
| 0.2141        | 7.0   | 427  | 0.2388          | 0.9221   | 0.7361 | 0.8061    | 0.6773 |
| 0.1802        | 8.0   | 488  | 0.2059          | 0.9278   | 0.7413 | 0.8707    | 0.6454 |
| 0.1494        | 9.0   | 549  | 0.2064          | 0.9221   | 0.7532 | 0.7657    | 0.7412 |
| 0.1179        | 10.0  | 610  | 0.1786          | 0.9401   | 0.8098 | 0.8245    | 0.7955 |
| 0.0941        | 11.0  | 671  | 0.1589          | 0.9457   | 0.8227 | 0.8632    | 0.7859 |
| 0.0816        | 12.0  | 732  | 0.1544          | 0.9477   | 0.8277 | 0.8781    | 0.7827 |


### Framework versions

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