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--- |
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datasets: |
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- squad_v2 |
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--- |
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# Roberta-LARGE finetuned on SQuADv2 |
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This is roberta-large model finetuned on SQuADv2 dataset for question answering answerability classification |
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## Model details |
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This model is simply an Sequenceclassification model with two inputs (context and question) in a list. |
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The result is either [1] for answerable or [0] if it is not answerable. |
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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. |
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## Model training |
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This model was trained with following parameters using simpletransformers wrapper: |
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``` |
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train_args = { |
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'learning_rate': 1e-5, |
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'max_seq_length': 512, |
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'overwrite_output_dir': True, |
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'reprocess_input_data': False, |
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'train_batch_size': 4, |
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'num_train_epochs': 4, |
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'gradient_accumulation_steps': 2, |
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'no_cache': True, |
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'use_cached_eval_features': False, |
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'save_model_every_epoch': False, |
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'output_dir': "bart-squadv2", |
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'eval_batch_size': 8, |
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'fp16_opt_level': 'O2', |
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} |
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``` |
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## Results |
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```{"accuracy": 90.48%}``` |
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## Model in Action 🚀 |
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```python3 |
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from simpletransformers.classification import ClassificationModel |
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model = ClassificationModel('roberta', 'a-ware/roberta-large-squadv2', num_labels=2, args=train_args) |
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predictions, raw_outputs = model.predict([["my dog is an year old. he loves to go into the rain", "how old is my dog ?"]]) |
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print(predictions) |
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==> [1] |
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``` |
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> Created with ❤️ by A-ware UG [](https://github.com/aware-ai) |
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