---
license: mit
base_model: microsoft/mdeberta-v3-base
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: acer_nitro_mdberta
  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. -->

# acer_nitro_mdberta

This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7384
- F1: 0.7593
- Roc Auc: 0.8588
- Accuracy: 0.6506

## 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: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| No log        | 1.0   | 166  | 0.5907          | 0.7281 | 0.8497  | 0.5904   |
| No log        | 2.0   | 332  | 0.5260          | 0.6836 | 0.8576  | 0.5181   |
| No log        | 3.0   | 498  | 0.7023          | 0.7324 | 0.8381  | 0.6024   |
| 0.3153        | 4.0   | 664  | 0.7848          | 0.7245 | 0.8168  | 0.5904   |
| 0.3153        | 5.0   | 830  | 0.6979          | 0.7436 | 0.8666  | 0.5904   |
| 0.3153        | 6.0   | 996  | 0.8550          | 0.7426 | 0.8337  | 0.6265   |
| 0.1464        | 7.0   | 1162 | 0.7102          | 0.7830 | 0.8700  | 0.6747   |
| 0.1464        | 8.0   | 1328 | 0.7172          | 0.7721 | 0.8662  | 0.6627   |
| 0.1464        | 9.0   | 1494 | 0.7812          | 0.7664 | 0.8613  | 0.6506   |
| 0.0781        | 10.0  | 1660 | 0.7384          | 0.7593 | 0.8588  | 0.6506   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0