| language: | |
| - en | |
| tags: | |
| - intent detection | |
| license: | |
| - License: other | |
| datasets: | |
| - intent expressions | |
| metrics: | |
| - accuracy | |
| ## 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). If you use this model, please cite our work. | |
| 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 | |