DistilBERT--SQuAD-v1

Training is done on the SQuAD dataset. The model can be accessed via HuggingFace:

Model Specifications

We have used the following parameters:

Training Batch Size : 512
Learning Rate : 3e-5
Training Epochs : 0.75
Sequence Length : 384
Stride : 128

Usage Specifications


from transformers import AutoModelForQuestionAnswering,AutoTokenizer,pipeline
model=AutoModelForQuestionAnswering.from_pretrained('abhilash1910/distilbert-squadv1')
tokenizer=AutoTokenizer.from_pretrained('abhilash1910/distilbert-squadv1')
nlp_QA=pipeline('question-answering',model=model,tokenizer=tokenizer)
QA_inp={
    'question': 'What is the fund price of Huggingface in NYSE?',
    'context': 'Huggingface Co. has a total fund price of $19.6 million dollars'
}
result=nlp_QA(QA_inp)
result

The result is:


{'score': 0.38547369837760925,
 'start': 42,
 'end': 55,
 'answer': '$19.6 million'}

language:

en license: apache-2.0 datasets:
squad_v1