---
license: mit
base_model: microsoft/deberta-v2-xxlarge
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: output
  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. -->

# output

This model is a fine-tuned version of [microsoft/deberta-v2-xxlarge](https://huggingface.co/microsoft/deberta-v2-xxlarge) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7550
- Accuracy: 0.6786
- Macro F1: 0.6773

## 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: 3e-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Macro F1 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|
| 1.6193        | 0.2286 | 100  | 1.6018          | 0.2184   | 0.1357   |
| 1.305         | 0.4571 | 200  | 0.9285          | 0.591    | 0.5953   |
| 0.8772        | 0.6857 | 300  | 0.8561          | 0.6256   | 0.6250   |
| 0.8552        | 0.9143 | 400  | 0.8332          | 0.6511   | 0.6473   |
| 0.798         | 1.1429 | 500  | 0.8210          | 0.6641   | 0.6579   |
| 0.7713        | 1.3714 | 600  | 0.7759          | 0.666    | 0.6669   |
| 0.7758        | 1.6    | 700  | 0.7634          | 0.6667   | 0.6615   |
| 0.7442        | 1.8286 | 800  | 0.7960          | 0.6613   | 0.6590   |
| 0.752         | 2.0571 | 900  | 0.7715          | 0.667    | 0.6690   |
| 0.7123        | 2.2857 | 1000 | 0.7600          | 0.6696   | 0.6698   |
| 0.7066        | 2.5143 | 1100 | 0.7599          | 0.6701   | 0.6684   |
| 0.7024        | 2.7429 | 1200 | 0.7551          | 0.6757   | 0.6763   |
| 0.7117        | 2.9714 | 1300 | 0.7550          | 0.6786   | 0.6773   |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.2.2
- Datasets 2.19.0
- Tokenizers 0.19.1