Model Details of xlm-roberta-base-squad2-distilled
deepset/xlm-roberta-base-squad2-distilled
haystack's distillation feature was used for training. deepset/xlm-roberta-large-squad2 was used as the teacher model.
Overview
Language model:
deepset/xlm-roberta-base-squad2-distilled
Language:
Multilingual
Downstream-task:
Extractive QA
Training data:
SQuAD 2.0
Eval data:
SQuAD 2.0
Code:
See
an example QA pipeline on Haystack
Infrastructure
: 1x Tesla v100
Haystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model in
Haystack
:
reader = FARMReader(model_name_or_path="deepset/xlm-roberta-base-squad2-distilled")
# or
reader = TransformersReader(model_name_or_path="deepset/xlm-roberta-base-squad2-distilled",tokenizer="deepset/xlm-roberta-base-squad2-distilled")
For a complete example of
deepset/xlm-roberta-base-squad2-distilled
being used for [question answering], check out the
Tutorials in Haystack Documentation
In Transformers
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
model_name = "deepset/xlm-roberta-base-squad2-distilled"# a) Get predictions
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
QA_input = {
'question': 'Why is model conversion important?',
'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.'
}
res = nlp(QA_input)
# b) Load model & tokenizer
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
deepset
is the company behind the open-source NLP framework
Haystack
which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc.
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