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deepset / roberta-base-squad2
Model's Last Updated: September 24 2024
question-answering
Introduction of roberta-base-squad2
Model Details of roberta-base-squad2
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.
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-base-squad2")
# or
reader = TransformersReader(model_name_or_path="deepset/roberta-base-squad2",tokenizer="deepset/roberta-base-squad2")
For a complete example of
roberta-base-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-base-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)
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:
[email protected]
Timo Möller:
[email protected]
Malte Pietsch:
[email protected]
Tanay Soni:
[email protected]
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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More Information About roberta-base-squad2 huggingface.co Model
Url of roberta-base-squad2
roberta-base-squad2 huggingface.co Url
Provider of roberta-base-squad2 huggingface.co
Other API from deepset
Updated: September 26 2024
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