metadata

license: apache-2.0
datasets:
  - legacy-datasets/banking77
language:
  - en
metrics:
  - accuracy
base_model:
  - bert-base-uncased
pipeline:
  - GEM_pipeline

GEM_Banking77 Model Card

This model card provides an overview of the GEM_Banking77 model, a fine-tuned implementation of the GEM architecture designed for the Banking77 dataset.

Purpose

The GEM_Banking77 model was developed to evaluate the performance of the GEM architecture on domain-specific datasets, particularly in the banking and financial sector. The Banking77 dataset, a benchmark for intent classification, was chosen to assess the model’s effectiveness.

Key Details

License: Apache-2.0
Dataset: legacy-datasets/banking77
Language: English
Metrics: Accuracy: 92.56%
Base Model: bert-base-uncased
Pipeline: GEM_pipeline

Model Details

The GEM_Banking77 model is built on the GEM architecture and fine-tuned from bert-base-uncased using the Banking77 dataset. The model configuration is as follows:

Number of epochs: 10
Batch size: Dynamic scaling: 32 * number of GPUs
Learning rate: 2e-5
Maximum sequence length: 128
Gradient accumulation steps: 2
Cluster size: 256
Number of domains: 8
Number of classes: 77
Number of attention heads: 12

Training & Evaluation

The model was trained using the GEM_pipeline and evaluated using accuracy, achieving a score of 92.56%.