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Open-weight model · Question answering

bert-base-cased-squad2

by Deepset deepset/bert-base-cased-squad2

This is a BERT base cased model trained on SQuAD v2 Haystack is an AI orchestration framework to build customizable, production-ready LLM applications. You can use this model in Haystack to do extractive question answering on documents.

Parameters108M
Context512
Weights1.3 GB
Licensecc-by-4.0
AccessOpen weights
Monthly Downloads71.7k

Runs On

What it takes to serve bert-base-cased-squad2 (108M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

PrecisionWeightsMemory neededCheapest setupPer hourAlso fits
16-bit 0.2 GB 0.3 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
8-bit 0.1 GB 0.1 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
4-bit 0.1 GB 0.1 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00

Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Sep 18, 2026.

Model Card

By Deepset, published under cc-by-4.0, revision d378e310c800.

This is a BERT base cased model trained on SQuAD v2 Haystack is an AI orchestration framework to build customizable, production-ready LLM applications. You can use this model in Haystack to do extractive question answering on documents. To load and run the model with Haystack: For a complete example with an extractive question answering pipeline that scales over many documents, check out the corresponding Haystack tutorial. deepset is the company behind the production-ready open-source AI framework Haystack. We also have a Discord community open to everyone!

Read Deepset's full model card

This is a BERT base cased model trained on SQuAD v2

Overview

Language model: bert-base-cased Language: English
Downstream-task: Extractive QA
Training data: SQuAD 2.0
Eval data: SQuAD 2.0
Code: See an example extractive QA pipeline built with Haystack

Usage

In Haystack

Haystack is an AI orchestration framework to build customizable, production-ready LLM applications. You can use this model in Haystack to do extractive question answering on documents. To load and run the model with Haystack:

# After running pip install haystack-ai "transformers[torch,sentencepiece]"

from haystack import Document
from haystack.components.readers import ExtractiveReader

docs = [
    Document(content="Python is a popular programming language"),
    Document(content="python ist eine beliebte Programmiersprache"),
]

reader = ExtractiveReader(model="deepset/bert-base-cased-squad2")
reader.warm_up()

question = "What is a popular programming language?"
result = reader.run(query=question, documents=docs)
# {'answers': [ExtractedAnswer(query='What is a popular programming language?', score=0.5740374326705933, data='python', document=Document(id=..., content: '...'), context=None, document_offset=ExtractedAnswer.Span(start=0, end=6),...)]}

For a complete example with an extractive question answering pipeline that scales over many documents, check out the corresponding Haystack tutorial.

In Transformers

from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline

model_name = "deepset/bert-base-cased-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)

About us

deepset is the company behind the production-ready open-source AI framework Haystack.

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 | YouTube

By the way: we're hiring!

Configuration

Architecture
BertForQuestionAnswering
Context length (tokens)
512
Layers
12
Hidden size
768
Feed-forward size
3,072
Attention heads
12
Vocabulary size
28,996
Model type
bert

Identity and Version

Repository
deepset/bert-base-cased-squad2
Publisher
Deepset
Task
Question answering
Modality
Text
Library
transformers
Parameters
108M parameters
Languages
en
Revision
d378e310c8000d824954c1e76c943a0581b49f0c
First published
2022-03-02
Last updated
2024-09-24

Files and Weights

10 files, 1.7 GB in total. The weights are 3 files totalling 1.3 GB in bin, msgpack, safetensors.

Weights3 files · 1.3 GB
Configuration2 files · 620 B
Tokenizer2 files · 213.6 KB
Documentation1 file · 5.1 KB
Other1 file · 402.3 MB
Repository1 file · 445 B
Every file
FileTypeSizeSHA-256
flax_model.msgpackWeights430.9 MB b66ccf30121d
model.safetensorsWeights433.3 MB 2f69606d70f7
pytorch_model.binWeights433.3 MB 4f7cda139f23
config.jsonConfiguration508 B
special_tokens_map.jsonConfiguration112 B
README.mdDocumentation5.1 KB
saved_model.tar.gzOther402.3 MB d6361458d70b
.gitattributesRepository445 B
tokenizer_config.jsonTokenizer152 B
vocab.txtTokenizer213.4 KB

License and Download

License
cc-by-4.0
Access
Open weights, no gate
Download size
1.3 GB
Download from Deepset

Released by Deepset through its official repository on Hugging Face. Read the license.

Built From

  • Trained on (disclosed) squad_v2

Evaluations

Each result is shown as reported, with the conditions its reporter stated. None is a SAVRN measurement. A comparison lines two results up only when their configuration, unit and setup are all stated and identical.

BenchmarkConditionsResultReported byRevisionDate
squad_v2 Configuration squad_v2Task Question AnsweringMetric Exact MatchComparison conditions not established 71.1517 deepset
Publisher reported
Evaluated revision not stated
squad_v2 Configuration squad_v2Task Question AnsweringMetric F1Comparison conditions not established 74.6714 deepset
Publisher reported
Evaluated revision not stated

Memory Requirements

PrecisionWeights in memory
As published1.3 GB
16-bit0.2 GB
8-bit0.1 GB
4-bit0.1 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Questions About bert-base-cased-squad2

How much GPU memory does bert-base-cased-squad2 need?

About 0.3 GB at 16-bit and 0.1 GB at 4-bit: the weights (108M parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run bert-base-cased-squad2 on?

At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.

Can I use bert-base-cased-squad2 commercially?

Yes. bert-base-cased-squad2 is released under Creative Commons Attribution 4.0. CC BY 4.0 permits sharing and adapting the work, including commercially, provided the creator is credited and changes are indicated.

What is bert-base-cased-squad2's context length?

512 tokens, from the maximum position embeddings in its published configuration.

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