---
license: mit
base_model: microsoft/deberta-v3-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-v3-large-zeroshot-v2.0-2024-04-01-09-59
  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. -->

# deberta-v3-large-zeroshot-v2.0-2024-04-01-09-59

This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1590
- F1 Macro: 0.6865
- F1 Micro: 0.7525
- Accuracy Balanced: 0.7303
- Accuracy: 0.7525
- Precision Macro: 0.6989
- Recall Macro: 0.7303
- Precision Micro: 0.7525
- Recall Micro: 0.7525

## 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: 9e-06
- train_batch_size: 4
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step  | Validation Loss | F1 Macro | F1 Micro | Accuracy Balanced | Accuracy | Precision Macro | Recall Macro | Precision Micro | Recall Micro |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:-----------------:|:--------:|:---------------:|:------------:|:---------------:|:------------:|
| 0.1929        | 1.0   | 43383 | 0.3181          | 0.8694   | 0.8813   | 0.8688            | 0.8813   | 0.8701          | 0.8688       | 0.8813          | 0.8813       |
| 0.1358        | 2.0   | 86766 | 0.3519          | 0.8730   | 0.8849   | 0.8713            | 0.8849   | 0.8749          | 0.8713       | 0.8849          | 0.8849       |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.2+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2