readme: ---
  language:
  - en
  tags:
  - intent detection
  license: "other"
  datasets:
  - ibm/vira-intents
  metrics:
  - accuracy
  widget:
  - text: "Should I be concerned about side effects of the vaccine if I'm breastfeeding?} & Is breastfeeding safe with the vaccine"
    example_title: "Breastfeeding"
  - text: "Does the vaccine prevent transmission?"
    example_title: "Transmission"
  - text: "Will the vaccine make me sterile or infertile?	"
    example_title: "Infertility" 
  ---
  
  ## Model Description
  This model is based on RoBERTa large (Liu, 2019), fine-tuned on a dataset of intent expressions available [here](https://research.ibm.com/haifa/dept/vst/debating_data.shtml) and also on 🤗 Transformer datasets hub [here](https://huggingface.co/datasets/ibm/vira-intents).
  
  The model was created as part of the work described in [Benchmark Data and Evaluation Framework for Intent Discovery Around COVID-19 Vaccine Hesitancy
  ](https://arxiv.org/abs/2205.11966). The model is released under the Community Data License Agreement - Sharing - Version 1.0 ([link](https://cdla.dev/sharing-1-0/)), If you use this model, please cite our paper.
  
  The official GitHub is [here](https://github.com/IBM/vira-intent-discovery). The script used for training the model is [trainer.py](https://github.com/IBM/vira-intent-discovery/blob/master/trainer.py).
  
  
  ## Training parameters
  1. base_model = 'roberta-large'
  1. learning_rate=5e-6
  1. per_device_train_batch_size=16,
  1. per_device_eval_batch_size=16,
  1. num_train_epochs=15,
  1. load_best_model_at_end=True,
  1. save_total_limit=1,
  1. save_strategy='epoch',
  1. evaluation_strategy='epoch',
  1. metric_for_best_model='accuracy',
  1. seed=123
  
  ## Data collator
  DataCollatorWithPadding
  