---
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: out_2
  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. -->

# out_2

This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6774
- F1: 0.7444

## 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: 3
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 48
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 5.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Accuracy | Validation Loss |
|:-------------:|:-----:|:-----:|:--------:|:---------------:|
| 0.6448        | 0.21  | 500   | 0.6347   | 0.6498          |
| 0.6401        | 0.41  | 1000  | 0.6442   | 0.6312          |
| 0.6557        | 0.62  | 1500  | 0.6582   | 0.6314          |
| 0.5819        | 0.83  | 2000  | 0.6588   | 0.6320          |
| 0.6086        | 1.04  | 2500  | 0.6563   | 0.6343          |
| 0.6011        | 1.24  | 3000  | 0.6557   | 0.6165          |
| 0.5616        | 1.45  | 3500  | 0.6461   | 0.6376          |
| 0.5885        | 1.66  | 4000  | 0.6468   | 0.6304          |
| 0.6198        | 1.87  | 4500  | 0.6423   | 0.6448          |
| 0.5838        | 2.07  | 5000  | 0.6665   | 0.6320          |
| 0.5564        | 2.28  | 5500  | 0.6684   | 0.6428          |
| 0.5726        | 2.49  | 6000  | 0.6703   | 0.6401          |
| 0.5491        | 2.7   | 6500  | 0.6684   | 0.6455          |
| 0.5303        | 2.9   | 7000  | 0.6703   | 0.6339          |
| 0.497         | 3.11  | 7500  | 0.6607   | 0.6541          |
| 0.5041        | 3.32  | 8000  | 0.6760   | 0.6653          |
| 0.4978        | 3.53  | 8500  | 0.6696   | 0.6627          |
| 0.5272        | 3.73  | 9000  | 0.6677   | 0.6684          |
| 0.5487        | 3.94  | 9500  | 0.6760   | 0.6593          |
| 0.4998        | 4.15  | 10000 | 0.6747   | 0.6738          |
| 0.4626        | 4.36  | 10500 | 0.6753   | 0.6781          |
| 0.5202        | 4.56  | 11000 | 0.6722   | 0.6763          |
| 0.4623        | 4.77  | 11500 | 0.6728   | 0.6778          |
| 0.4383        | 4.98  | 12000 | 0.6741   | 0.6775          |


### Framework versions

- Transformers 4.35.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.14.1