---
license: mit
base_model: MoritzLaurer/deberta-v3-base-zeroshot-v1.1-all-33
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: zeroshot-classification-test4
  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. -->

# zeroshot-classification-test4

This model is a fine-tuned version of [MoritzLaurer/deberta-v3-base-zeroshot-v1.1-all-33](https://huggingface.co/MoritzLaurer/deberta-v3-base-zeroshot-v1.1-all-33) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4552
- F1 Macro: 0.8589
- F1 Micro: 0.859
- Accuracy Balanced: 0.8590
- Accuracy: 0.859
- Precision Macro: 0.8596
- Recall Macro: 0.8590
- Precision Micro: 0.859
- Recall Micro: 0.859

## 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: 32
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 3
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------------:|:--------:|:---------------:|:------------:|:---------------:|:------------:|
| No log        | 1.0   | 313  | 0.3407          | 0.8507   | 0.851    | 0.8509            | 0.851    | 0.8534          | 0.8509       | 0.851           | 0.851        |
| 0.373         | 2.0   | 626  | 0.3830          | 0.8600   | 0.86     | 0.8600            | 0.86     | 0.86            | 0.8600       | 0.86            | 0.86         |
| 0.373         | 3.0   | 939  | 0.4552          | 0.8589   | 0.859    | 0.8590            | 0.859    | 0.8596          | 0.8590       | 0.859           | 0.859        |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1