---
library_name: transformers
license: mit
base_model: MoritzLaurer/deberta-v3-large-zeroshot-v2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: results
  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. -->

# results

This model is a fine-tuned version of [MoritzLaurer/deberta-v3-large-zeroshot-v2.0](https://huggingface.co/MoritzLaurer/deberta-v3-large-zeroshot-v2.0) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2757
- Accuracy: 0.8113
- F1 Macro: 0.7264
- Precision Neutral: 0.8559
- Recall Neutral: 0.8920
- F1 Neutral: 0.8736
- Support Neutral: 213
- Precision Entailment: 0.7436
- Recall Entailment: 0.5472
- F1 Entailment: 0.6304
- Support Entailment: 53
- Precision Contradiction: 0.6341
- Recall Contradiction: 0.7222
- F1 Contradiction: 0.6753
- Support Contradiction: 36

## 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 128
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | Precision Neutral | Recall Neutral | F1 Neutral | Support Neutral | Precision Entailment | Recall Entailment | F1 Entailment | Support Entailment | Precision Contradiction | Recall Contradiction | F1 Contradiction | Support Contradiction |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------------:|:--------------:|:----------:|:---------------:|:--------------------:|:-----------------:|:-------------:|:------------------:|:-----------------------:|:--------------------:|:----------------:|:---------------------:|
| 0.0311        | 1.0   | 383  | 0.2757          | 0.8113   | 0.7264   | 0.8559            | 0.8920         | 0.8736     | 213             | 0.7436               | 0.5472            | 0.6304        | 53                 | 0.6341                  | 0.7222               | 0.6753           | 36                    |
| 0.0179        | 2.0   | 766  | 0.3661          | 0.7781   | 0.6858   | 0.8387            | 0.8545         | 0.8465     | 213             | 0.6226               | 0.6226            | 0.6226        | 53                 | 0.625                   | 0.5556               | 0.5882           | 36                    |
| 0.003         | 3.0   | 1149 | 0.4953          | 0.8013   | 0.7063   | 0.8341            | 0.8967         | 0.8643     | 213             | 0.7045               | 0.5849            | 0.6392        | 53                 | 0.6897                  | 0.5556               | 0.6154           | 36                    |
| 0.0008        | 4.0   | 1532 | 0.5224          | 0.7980   | 0.6984   | 0.8341            | 0.8967         | 0.8643     | 213             | 0.6977               | 0.5660            | 0.625         | 53                 | 0.6667                  | 0.5556               | 0.6061           | 36                    |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1