---
license: mit
base_model: microsoft/deberta-v3-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-v3-large-zeroshot-v1.1-all_except_nli
  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-v1.1-all_except_nli

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: 0.3183
- F1 Macro: 0.2132
- F1 Micro: 0.2379
- Accuracy Balanced: 0.2319
- Accuracy: 0.2379
- Precision Macro: 0.4070
- Recall Macro: 0.2319
- Precision Micro: 0.2379
- Recall Micro: 0.2379

## 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: 16
- eval_batch_size: 64
- 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: 3

### 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   | 27664 | 0.3072          | 0.8708   | 0.8831   | 0.8683            | 0.8831   | 0.8735          | 0.8683       | 0.8831          | 0.8831       |
| 0.1426        | 2.0   | 55328 | 0.3692          | 0.8709   | 0.8839   | 0.8664            | 0.8839   | 0.8761          | 0.8664       | 0.8839          | 0.8839       |
| 0.0935        | 3.0   | 82992 | 0.4419          | 0.8747   | 0.8864   | 0.8729            | 0.8864   | 0.8765          | 0.8729       | 0.8864          | 0.8864       |


### Framework versions

- Transformers 4.33.3
- Pytorch 1.11.0+cu113
- Datasets 2.14.6
- Tokenizers 0.12.1