roberta-large for QA
This is the roberta-large model, fine-tuned using the SQuAD2.0 dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering.
Overview
Language model: roberta-large
Language: English
Downstream-task: Extractive QA
Training data: SQuAD 2.0
Eval data: SQuAD 2.0
Code: See an example QA pipeline on Haystack
Infrastructure: 4x Tesla v100
Hyperparameters
base_LM_model = "roberta-large"
Using a distilled model instead
Please note that we have also released a distilled version of this model called deepset/roberta-base-squad2-distilled. The distilled model has a comparable prediction quality and runs at twice the speed of the large model.
Usage
In Haystack
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/roberta-large-squad2")
# or
reader = TransformersReader(model_name_or_path="deepset/roberta-large-squad2",tokenizer="deepset/roberta-large-squad2")
For a complete example of roberta-large-squad2
being used for Question Answering, check out the Tutorials in Haystack Documentation
In Transformers
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
model_name = "deepset/roberta-large-squad2"
# 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)
Authors
Branden Chan: branden.chan@deepset.ai
Timo Möller: timo.moeller@deepset.ai
Malte Pietsch: malte.pietsch@deepset.ai
Tanay Soni: tanay.soni@deepset.ai
About us


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.
Some of our other work:
- Distilled roberta-base-squad2 (aka "tinyroberta-squad2")
- German BERT (aka "bert-base-german-cased")
- GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")
Get in touch and join the Haystack community
For more info on Haystack, visit our GitHub repo and Documentation.
We also have a Discord community open to everyone!
Twitter | LinkedIn | Discord | GitHub Discussions | Website
By the way: we're hiring!
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Evaluation results
- Exact Match on squad_v2validation set self-reported85.168
- F1 on squad_v2validation set self-reported88.349
- Exact Match on squadvalidation set self-reported87.162
- F1 on squadvalidation set self-reported93.603
- Exact Match on adversarial_qavalidation set self-reported35.900
- F1 on adversarial_qavalidation set self-reported48.923
- Exact Match on squad_adversarialvalidation set self-reported81.142
- F1 on squad_adversarialvalidation set self-reported87.099
- Exact Match on squadshifts amazontest set self-reported72.453
- F1 on squadshifts amazontest set self-reported86.325
- Exact Match on squadshifts new_wikitest set self-reported82.338
- F1 on squadshifts new_wikitest set self-reported91.974
- Exact Match on squadshifts nyttest set self-reported84.352
- F1 on squadshifts nyttest set self-reported92.645
- Exact Match on squadshifts reddittest set self-reported74.722
- F1 on squadshifts reddittest set self-reported86.860