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---
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
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