
## 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

{"language": ["en"], "license": "other", "tags": ["intent detection"], "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?\t", "example_title": "Infertility"}]}
transformers pytorch roberta text-classification intent detection en dataset:ibm/vira-intents arxiv:2205.11966 license:other autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #intent detection #en #dataset-ibm/vira-intents #arxiv-2205.11966 #license-other #autotrain_compatible #endpoints_compatible #region-us 


## Model Description
This model is based on RoBERTa large (Liu, 2019), fine-tuned on a dataset of intent expressions available here and also on  Transformer datasets hub here.

The model was created as part of the work described in Benchmark Data and Evaluation Framework for Intent Discovery Around COVID-19 Vaccine Hesitancy
. The model is released under the Community Data License Agreement - Sharing - Version 1.0 (link), If you use this model, please cite our paper.

The official GitHub is here. The script used for training the model is URL.


## 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

## Model Description
This model is based on RoBERTa large (Liu, 2019), fine-tuned on a dataset of intent expressions available here and also on  Transformer datasets hub here.

The model was created as part of the work described in Benchmark Data and Evaluation Framework for Intent Discovery Around COVID-19 Vaccine Hesitancy
. The model is released under the Community Data License Agreement - Sharing - Version 1.0 (link), If you use this model, please cite our paper.

The official GitHub is here. The script used for training the model is URL. ## 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
TAGS
#transformers #pytorch #roberta #text-classification #intent detection #en #dataset-ibm/vira-intents #arxiv-2205.11966 #license-other #autotrain_compatible #endpoints_compatible #region-us 
 ## Model Description
This model is based on RoBERTa large (Liu, 2019), fine-tuned on a dataset of intent expressions available here and also on  Transformer datasets hub here.

The model was created as part of the work described in Benchmark Data and Evaluation Framework for Intent Discovery Around COVID-19 Vaccine Hesitancy
. The model is released under the Community Data License Agreement - Sharing - Version 1.0 (link), If you use this model, please cite our paper.

The official GitHub is here. The script used for training the model is URL. ## 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
passage: TAGS
#transformers #pytorch #roberta #text-classification #intent detection #en #dataset-ibm/vira-intents #arxiv-2205.11966 #license-other #autotrain_compatible #endpoints_compatible #region-us 
## Model Description
This model is based on RoBERTa large (Liu, 2019), fine-tuned on a dataset of intent expressions available here and also on  Transformer datasets hub here.

The model was created as part of the work described in Benchmark Data and Evaluation Framework for Intent Discovery Around COVID-19 Vaccine Hesitancy
. The model is released under the Community Data License Agreement - Sharing - Version 1.0 (link), If you use this model, please cite our paper.

The official GitHub is here. The script used for training the model is URL.## 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