squirro/distilroberta-base-squad_v2

distilroberta-base-squad_v2

This model is a fine-tuned version of distilroberta-base on the squad_v2 dataset.

Model description

This model is fine-tuned on the extractive question answering task -- The Stanford Question Answering Dataset -- SQuAD2.0.

For convenience this model is prepared to be used with the frameworks PyTorch, Tensorflow and ONNX.

Intended uses & limitations

This model can handle mismatched question-context pairs. Make sure to specify handle_impossible_answer=True when using QuestionAnsweringPipeline.

_Example usage:_

>>> from transformers import AutoModelForQuestionAnswering, AutoTokenizer, QuestionAnsweringPipeline
>>> model = AutoModelForQuestionAnswering.from_pretrained("squirro/distilroberta-base-squad_v2")
>>> tokenizer = AutoTokenizer.from_pretrained("squirro/distilroberta-base-squad_v2")
>>> qa_model = QuestionAnsweringPipeline(model, tokenizer)
>>> qa_model(
>>>    question="What's your name?",
>>>    context="My name is Clara and I live in Berkeley.",
>>>    handle_impossible_answer=True  # important!
>>> )
{'score': 0.9498472809791565, 'start': 11, 'end': 16, 'answer': 'Clara'}

Training and evaluation data

Training and evaluation was done on SQuAD2.0.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• —learning_rate: 5e-05
• —trainbatchsize: 64
• —evalbatchsize: 8
• —seed: 42
• —distributed_type: tpu
• —num_devices: 8
• —totaltrainbatch_size: 512
• —totalevalbatch_size: 64
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: linear
• —num_epochs: 3.0

Training results

Framework versions

• —Transformers 4.17.0.dev0
• —Pytorch 1.9.0+cu111
• —Datasets 1.18.3
• —Tokenizers 0.11.6

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