---
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-v3-base-zeroshot-v2.0-2024-03-21-22-15
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# deberta-v3-base-zeroshot-v2.0-2024-03-21-22-15

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: 0.1169
- F1 Macro: 0.5016
- F1 Micro: 0.5474
- Accuracy Balanced: 0.5434
- Accuracy: 0.5474
- Precision Macro: 0.6345
- Recall Macro: 0.5434
- Precision Micro: 0.5474
- Recall Micro: 0.5474

## 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: 16
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 2
- 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
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | F1 Macro | F1 Micro | Accuracy Balanced | Accuracy | Precision Macro | Recall Macro | Precision Micro | Recall Micro |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:-----------------:|:--------:|:---------------:|:------------:|:---------------:|:------------:|
| 0.2288        | 1.0   | 27331 | 0.6189          | 0.7688   | 0.7881   | 0.7705            | 0.7881   | 0.7673          | 0.7705       | 0.7881          | 0.7881       |
| 0.1559        | 2.0   | 54662 | 0.6059          | 0.7896   | 0.8082   | 0.7898            | 0.8082   | 0.7894          | 0.7898       | 0.8082          | 0.8082       |


### Framework versions

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