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---
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
datasets:
- generator
metrics:
- accuracy
model-index:
- name: deberta-v3-base-finetuned-mnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: generator
type: generator
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9165876777251185
---
<!-- 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-base-finetuned-mnli
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4279
- Accuracy: 0.9166
## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0003 | 1.0 | 374 | 0.6553 | 0.9137 |
| 0.1791 | 2.0 | 748 | 0.4279 | 0.9166 |
| 0.1101 | 3.0 | 1122 | 0.5088 | 0.9081 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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