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---
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
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