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Contributed by

a-ware A-ware UG
10 models

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 πŸš€

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 ?"]])
print(predictions)
==> [1]

Created with ❀️ by A-ware UG Github icon