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ByteDance_Seed_2_0_2026_09_08 — capture 20260912T062118Z

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Extracted text

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Public
Summary
of
Training
Data
Content

for
Seed
2.0
Pro


Version
of
the
Summary:

 1.0

Last
update:

 31
July
2026

1. General
information

1.1 Provider
identification


Provider
name
and
contact

details:


ByteDance
Nexus
AI
Pte.
Ltd
(ByteDance)

Authorised
representative

name
and
contact
details:

Mikros
Information
Technology
Ireland
Limited

1.2 Model
identification


Versioned
model
name(s):
 Seed
2.0-Pro
(Model
name
on
Byteplus:
Dola-Seed-2.0-Pro)


Model
dependencies:
 N/A

Date
of
placement
of
the

model
on
the
Union

market:


March
2026

1.3 Modalities,
overall
training
data
size
and
other
characteristics

Modality



Training
data
size



Types
of
content


Text



More
than
10
trillion
tokens The
model
was
pre-trained
using
a

large-scale,
diverse
collection
of
data

spanning
a
wide-range
of
domains

and
modalities,
including
datasets


containing
information
represented

as
written
language.

Image
 More
than
1
billion
images


The
model
was
pre-trained
using
a

large-scale,
diverse
collection
of
data

spanning
a
wide-range
of
domains

and
modalities,
including
datasets

containing
static
and
visual

information
(such
as
photographs
and

diagrams).

Audio
 N/A



N/A



Video

 10,000
to
1
million
hours
 The
model
was
pre-trained
using
a

large-scale,
diverse
collection
of
data

spanning
a
wide-range
of
domains

and
modalities,
including
datasets

containing
videoclips.

Other
 N/A
 N/A

Latest
date
of
data

acquisition/collection
for

model
training:

February
2026



Description
of
the
linguistic

characteristics
of
the
overall

training
data:


The
model's
training
data
contains
a
diverse
range
of
global
languages,

including
languages
from
within
Europe.



Other
relevant
characteristics

of
the
overall
training
data:

The
model
was
pre-trained
using
a
large-scale,
diverse
collection
of

data
spanning
a
wide-range
of
domains
and
modalities,
including

datasets
containing
information
represented
as
written
language,
static

and
visual
information
(such
as
photographs
and
diagrams)
and
video

clips.

Seed
2.0
Pro's
training
dataset
was
carefully
curated
to
maximize

diversity,
representativeness,
and
robustness,
while
appropriate

filtering
mechanisms
are
applied
to
filter
for
quality
and
safety
in

relation
to
low-quality
and
harmful
content.

Additional
comments

(optional):

N/A

2. List
of
Data
Sources


2.1 Publicly
available
datasets




Have
you
used
publicly
available

datasets
to
train
the
model?



Yes



If
yes,
specify
the
modality(ies)

of
the
content
covered
by
the

datasets
concerned:

Text,
image
and
video

List
of
large
publicly
available

datasets:


We
have
used
publicly
available
datasets
across
various
sectors
and

stages
of
the
model
development
pipeline.
These
publicly
available

datasets
are
subject
to
pre-processing
such
as
data
cleaning,

deduplication,
filtering,
selection,
etc
to
exclude
unsafe
or
harmful

content.

General
description
of
other

publicly
available
datasets
not

listed
above:

See
the
description
of
linguistic
characteristics
and
other
relevant

characteristics
of
overall
training
data
in
Section
1.


Additional
comments
(optional):
 N/A

2.2 Private
non-publicly
available
datasets
obtained
from
third

parties

2.2.1 Datasets
commercially
licensed
by
rightsholders
or
their

representatives

Have
you
concluded
transactional
commercial
licensing

agreement(s)
with
rightsholder(s)
or
with
their

representatives?

Yes



If
yes,
specify
the
modality(ies)
of
the
content
covered
by

the
datasets
concerned:


Text
and
image

2.2.2 Private
datasets
obtained
from
other
third
parties

Have
you
obtained
private
datasets
from

third
parties
that
are
not
licensed
as

described
in
Section
2.2.1,
such
as
data

obtained
from
providers
of
private
databases,

or
data
intermediaries?

Yes


If
yes,
specify
the
modality(ies)
of
the
content

covered
by
the
datasets
concerned:


Text
and
image

If
publicly
known,
list
private
datasets

obtained
from
other
third
parties:


N/A



General
description
of
non-publicly
known

private
datasets
obtained
from
third
parties


We
have
obtained
datasets
from
third-parties
on
a

licensed
basis.
Such
datasets
primarily
comprise

content
across
text
and
image
modalities.

Additional
comments
(optional):
 N/A

2.3 Data
crawled
and
scraped
from
online
sources

Were
crawlers
used
by
the
provider
or

on
behalf
of?

Yes

If
yes,
specify
crawler

name(s)/identifier(s):

Bytespider

Purposes
of
the
crawler(s):
 In
the
context
of
ByteDance's
AI
model
development,

Bytespider
is
used
to
collect
publicly
accessible
data,

including
text
and
image
data
to
support
training,
testing,

and
validation
of
the
AI
model.


General
description
of
crawler

behaviour:


ByteDance's
crawler
is
configured
to
avoid
overloading

websites
and
to
operate
in
a
manner
consistent
with
ethical

web
scraping
practices.
Our
crawler
is
designed
to
respect

robots.txt
instructions,
and
not
to
circumvent
control

measures
such
as
paywalls
or
to
access
password-protected

content.



Period
of
data
collection:
 Approximately
January
2022
to
June
2024.

Comprehensive
description
of
the
type

of
content
and
online
sources
crawled:

As
above,
we
may
use
our
crawler
to
collect
publicly

accessible
data.
The
crawled
content
primarily
consists
of

text
and
image
and
includes
a
variety
of
content
types

including
academic,
mathematical,
code-related
and
general-
purpose
content.



Type
of
modality
covered:
 Text
and
image

Summary
of
the
most
relevant
domain

names
crawled:

Crawled
content
includes
a
variety
of
publicly
accessible

online
materials
across
various
sectors,
such
as
education,

government,
technology
and
research
sectors.


Additional
comments
(optional):
 N/A

2.4 User
data

Was
data
from
user
interactions
with
the
AI
model

(e.g.
user
input
and
prompts)
used
to
train
the

model?

No

Was
data
collected
from
user
interactions
with
the

provider’s
other
services
or
products
used
to
train

the
model?




Yes

If
yes,
provide
a
general
description
of
the

provider’s
services
or
products
that
were
used
to

collect
the
user
data:

ByteDance's
models
are
trained
using
a

proprietary
mix
of
datasets,
which
tend
to
be

large-scale
and
diverse.
Such
industry-standard

datasets
typically
include
a
mixture
of
publicly

available
documents
as
described
in
this

disclosure.
Our
datasets
may
also
include
data

obtained
in
accordance
with
the
relevant
terms

and
conditions,
privacy
policy
and
pursuant
to

relevant
user-controls
as
applicable.


Type
of
modality
covered:
 See
above

Additional
comments
(optional):
 We
use
data
filtering
processes
to
reduce

personal
information
from
training
data,
and
to

reduce
the
amount
of
personal
data
in
our

training
data.


2.5 Synthetic
data

Was
synthetic
AI-generated
data
created
by
the

provider
or
on
their
behalf
to
train
the
model?



Yes

If
yes,
modality
of
the
synthetic
data:
 Text
and
image

If
yes,
specify
the
general-purpose
AI
model(s)
used

to
generate
the
synthetic
data
if
available
on
the

market:

Synthetic
data
was
generated
using
a
range
of

general-purpose
AI
models,
including
vision

language
models,
image
generation
models
and

large
language
models.


Information
about
other
AI
models,
including

provider’s
own
AI
model(s)
not
available
on
the

market,
used
to
generate
synthetic
data
to
train
the

model
to
which
this
Summary
applies:

We
may
use
fine-tuned
versions
of
internal

models
to
generate
synthetic
data.



Additional
comments
(optional):
 N/A

2.6 Other
sources
of
data

Have
data
sources
other
than
those
described
in

Sections
2.1
to
2.5
been
used
to
train
the
model?


Yes

If
yes,
provide
a
narrative
description
of
these
data

sources
and
the
data:



We
worked
with
our
vendors
to
create
labelled

datasets
so
as
to
improve
the
model's
capability

on
certain
tasks,
such
as
reasoning,
coding
and

knowledge
tasks.


Additional
comments
(optional):
 N/A

3. Data
processing
aspects

3.1 Respect
of
reservation
of
rights
from
text
and
data
mining

exception
or
limitation


Are
you
a
Signatory
to
the
Code
of
Practice
for
general-
purpose
AI
models
that
includes
commitments
to
respect

reservations
of
rights
from
the
TDM
exception
or
limitation?


No



Describe
the
measures
implemented
before
model
training

to
respect
reservations
of
rights
from
the
TDM
exception
or

limitation
before
and
during
data
collection,
including
the

opt-out
protocols
and
solutions
honoured
by
the
provider

or,
as
applicable,
by
third
parties
from
which
datasets
have

been
obtained:

ByteDance's
crawler
is
configured
to
avoid

overloading
websites
and
to
operate
in
a

manner
consistent
with
ethical
web

scraping
practices.
Our
crawler
is

designed
to
respect
robots.txt

instructions,
and
not
to
circumvent

control
measures
such
as
paywalls
or
to

access
password-protected
content.


Additional
comments
(optional):
 N/A

3.2 Removal
of
illegal
content


General
description
of
measures
taken:
 As
mentioned,
we
applied
preprocessing
and
filtering

methods,
including
filtering
models
focused
on
unsafe
or

harmful
content.


3.3 Other
information
(optional)

Other
relevant
information
about
data

processing
(optional):

N/A