- squad_v2
# Roberta-LARGE finetuned on SQuADv2
This is roberta-large model finetuned on SQuADv2 dataset for question answering answerability classification
## Model details
This model is simply an Sequenceclassification model with two inputs (context and question) in a list.
The result is either [1] for answerable or [0] if it is not answerable.
It was trained over 4 epochs on squadv2 dataset and can be used to filter out which context is good to give into the QA model to avoid bad answers.
## Model training
This model was trained with following parameters using simpletransformers wrapper:
train_args = {
'learning_rate': 1e-5,
'max_seq_length': 512,
'overwrite_output_dir': True,
'reprocess_input_data': False,
'train_batch_size': 4,
'num_train_epochs': 4,
'gradient_accumulation_steps': 2,
'no_cache': True,
'use_cached_eval_features': False,
'save_model_every_epoch': False,
'output_dir': "bart-squadv2",
'eval_batch_size': 8,
'fp16_opt_level': 'O2',
## Results
```{"accuracy": 90.48%}```
## Model in Action 🚀
from simpletransformers.classification import ClassificationModel
model = ClassificationModel('roberta', 'a-ware/roberta-large-squadv2', num_labels=2, args=train_args)
predictions, raw_outputs = model.predict([["my dog is an year old. he loves to go into the rain", "how old is my dog ?"]])
==> [1]
> Created with ❤️ by A-ware UG [![Github icon](https://cdn0.iconfinder.com/data/icons/octicons/1024/mark-github-32.png)](https://github.com/aware-ai)