qna2_deberta_model / README.md
nc33's picture
Update README.md
85e0e12
---
license: mit
tags:
- generated_from_trainer
datasets:
- super_glue
model-index:
- name: qna2_deberta_model
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. -->
# boolq_deberta_model
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the super_glue - boolq dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.4066
- eval_accuracy: 0.8468
- eval_runtime: 111.0255
- eval_samples_per_second: 29.453
- eval_steps_per_second: 1.846
- epoch: 2.0
- step: 1180
## 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
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2