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deepset/roberta-base-squad2

A pre-trained language model based on RoBERTa, fine-tuned on the SQuAD2.0 dataset for extractive question answering. It achieved scores of 79.87% exact match and 82.91% F1 score on the SQuAD2.0 dev set. Deepset is the company behind the open-source NLP framework Haystack, and offers other resources such as Distilled roberta-base-squad2, German BERT, and GermanQuAD datasets and models.

A pre-trained language model based on RoBERTa, fine-tuned on the SQuAD2.0 dataset for extractive question answering. It achieved scores of 79.87% exact match and 82.91% F1 score on the SQuAD2.0 dev set. Deepset is the company behind the open-source NLP framework Haystack, and offers other resources such as Distilled roberta-base-squad2, German BERT, and GermanQuAD datasets and models.

Public
$0.0005 / sec

Input

question relating to context

question source material

You need to login to use this model

Output

fox (0.18)

roberta-base for QA

This is the roberta-base 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-base 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

batch_size = 96
n_epochs = 2
base_LM_model = "roberta-base"
max_seq_len = 386
learning_rate = 3e-5
lr_schedule = LinearWarmup
warmup_proportion = 0.2
doc_stride=128
max_query_length=64

Using a distilled model instead

Please note that we have also released a distilled version of this model called deepset/tinyroberta-squad2. The distilled model has a comparable prediction quality and runs at twice the speed of the base model.

Performance

Evaluated on the SQuAD 2.0 dev set with the official eval script.

"exact": 79.87029394424324,
"f1": 82.91251169582613,

"total": 11873,
"HasAns_exact": 77.93522267206478,
"HasAns_f1": 84.02838248389763,
"HasAns_total": 5928,
"NoAns_exact": 81.79983179142137,
"NoAns_f1": 81.79983179142137,
"NoAns_total": 5945

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:

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!

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By the way: we're hiring!