llm-deberta-v3-swag / README.md
Paulo Vitor
fine tune swag
9adc396
---
language:
- en
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
datasets:
- swag
metrics:
- accuracy
model-index:
- name: llm-deberta-v3-swag
results:
- task:
name: Multiple Choice
type: multiple-choice
dataset:
name: SWAG
type: swag
config: regular
split: validation
args: regular
metrics:
- name: Accuracy
type: accuracy
value: 0.8679895997047424
---
<!-- 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. -->
# llm-deberta-v3-swag
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the SWAG dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7839
- Accuracy: 0.8680
## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0