---
license: mit
base_model: microsoft/deberta-v2-xxlarge
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: output1
  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. -->

# output1

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: 0.7690
- Accuracy: 0.676
- Macro F1: 0.6761

## 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: 6e-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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|
| 1.5278        | 0.2286 | 100  | 1.1249          | 0.5146   | 0.4600   |
| 0.9452        | 0.4571 | 200  | 0.8437          | 0.645    | 0.6425   |
| 0.8367        | 0.6857 | 300  | 0.8038          | 0.6477   | 0.6531   |
| 0.8092        | 0.9143 | 400  | 0.7801          | 0.6593   | 0.6611   |
| 0.7679        | 1.1429 | 500  | 0.7868          | 0.6717   | 0.6697   |
| 0.7451        | 1.3714 | 600  | 0.7711          | 0.6647   | 0.6645   |
| 0.7467        | 1.6    | 700  | 0.7646          | 0.6659   | 0.6649   |
| 0.7261        | 1.8286 | 800  | 0.7840          | 0.6649   | 0.6632   |
| 0.7305        | 2.0571 | 900  | 0.7755          | 0.6681   | 0.6707   |
| 0.6742        | 2.2857 | 1000 | 0.7719          | 0.6691   | 0.6707   |
| 0.6728        | 2.5143 | 1100 | 0.7640          | 0.6726   | 0.6726   |
| 0.6691        | 2.7429 | 1200 | 0.7759          | 0.6761   | 0.6783   |
| 0.677         | 2.9714 | 1300 | 0.7690          | 0.676    | 0.6761   |


### Framework versions

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