---
license: apache-2.0
datasets:
- qiaojin/PubMedQA
language:
- en
metrics:
- accuracy
base_model:
- google-bert/bert-base-uncased
---

# GEM_PubMedQA Model Card

This model card provides an overview of the GEM_PubMedQA model, a finetuned implementation of the GEM architecture designed for the PubMedQA dataset.

## Purpose

The GEM_PubMedQA model was developed to assess the performance of the GEM architecture on domain-specific datasets, with a focus on healthcare. The PubMedQA dataset, a key benchmark in this field, was selected to evaluate its effectiveness.

## Key Details

- **License**: Apache-2.0
- **Dataset**: qiaojin/PubMedQA
- **Language**: English
- **Metrics**: Accuracy: 92.5%
- **Base Model**: google-bert/bert-base-uncased

## Model Details

The GEM_PubMedQA model is built on the GEM architecture and finetuned from the `google-bert/bert-base-uncased` model using the PubMedQA dataset. The training was performed with the following parameters:

- **Number of epochs**: 5
- **Batch size**: 128
- **Learning rate**: 2e-5
- **Maximum sequence length**: 128
- **Gradient accumulation steps**: 2
- **Cluster size**: 256
- **Threshold**: 0.65