Neural Machine Translation (NMT) model
Google's standard Neural Machine Translation (NMT) has evolved from the neural network translation system that Google introduced in November 2016, with many improvements. It can translate to and from many languages in real time.
Its model ID is general/nmt
.
You can access the NMT model without modification through either the Cloud Translation - Basic API or the Cloud Translation - Advanced API. With Cloud Translation - Advanced API, you can also customize it .
In either case, you need to have a Google Cloud project that has Cloud Translation API enabled, with credentials to make authenticated calls. To access the model using Python or another programming language, install the appropriate v2 or v3 client library.
In the examples shown here, PROJECT_ID
represents your project ID, and REGION_NAME
represents the technical region name of the Google Cloud region
where you want to run the translation operation (such as us-central1
).
Use ISO-639
codes to identify source
and target languages where necessary.
Cloud Translation - Basic API REST example
You can make a REST call with Cloud Translation - Basic API to TranslateText
using the
NMT model. You can put request fields into a JSON
file named something like request.json
, like this:
{
"q"
:
[
"Hello world"
,
"My name is Jeff"
],
"target"
:
"de"
}
The Cloud Translation - Basic API only supports the NMT model, so you don't need to specify the model in the request.
You can then use a curl
command to submit the request:
!curl -X POST \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "x-goog-user-project: cloud-ml-translate-e2e-testing" \
-H "Content-Type: application/json; charset=utf-8" \
-d @request.json \
"https://translate.googleapis.com/language/translate/v2"
The response is a JSON document which looks like this:
{
"data"
:
{
"translations"
:
[
{
"translatedText"
:
"Hallo Welt"
,
"detectedSourceLanguage"
:
"en"
},
{
"translatedText"
:
"Mein Name ist Jeff"
,
"detectedSourceLanguage"
:
"en"
}
]
}
}
Cloud Translation - Basic API Python example
Here is sample Python code for calling TranslateText
on the
NMT model using the Cloud Translation - Basic API.
Because no source language is specified, the API attempts to
detect it automatically.
def
translate_text
(
text
:
str
|
bytes
|
list
[
str
]
=
"¡Hola amigos y amigas!"
,
target_language
:
str
=
"en"
,
source_language
:
str
|
None
=
None
,
)
-
> dict
:
translate_client
=
translate_v2
.
Client
()
if
isinstance
(
text
,
bytes
):
text
=
[
text
.
decode
(
"utf-8"
)]
if
isinstance
(
text
,
str
):
text
=
[
text
]
results
=
translate_client
.
translate
(
values
=
text
,
target_language
=
target_language
,
source_language
=
source_language
)
for
result
in
results
:
if
"detectedSourceLanguage"
in
result
:
print
(
f
"Detected source language:
{
result
[
'detectedSourceLanguage'
]
}
"
)
print
(
f
"Input text:
{
result
[
'input'
]
}
"
)
print
(
f
"Translated text:
{
result
[
'translatedText'
]
}
"
)
print
()
return
results
translate_text
()
The output is as follows:
Detected source language: es
Input text: ¡Hola amigos y amigas!
Translated text: Hello friends!
[{'translatedText': 'Hello friends!',
'detectedSourceLanguage': 'es',
'input': '¡Hola amigos y amigas!'}]
Cloud Translation - Advanced API REST example
You can make a REST call with Cloud Translation - Advanced API to TranslateText
using the default
NMT model either by not specifying a model, or by
explicitly requesting the NMT model. Put request
fields into a JSON file named something like request.json
, like this:
{
"sourceLanguageCode"
:
"en"
,
"targetLanguageCode"
:
"ru"
,
"contents"
:
[
"Dr. Watson, come here!"
,
"Bring me some coffee!"
],
"model"
:
"projects/PROJECT_ID/locations/REGION_NAME/models/general/nmt"
}
You can then use a curl
command to submit the request:
curl -X POST \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "x-goog-user-project: PROJECT_ID" \
-H "Content-Type: application/json; charset=utf-8" \
-d @request.json \
"https://translation.googleapis.com/v3/projects/PROJECT_ID/locations/REGION_NAME:translateText"
The response is a JSON document which looks like this:
{
"translations"
:
[
{
"translatedText"
:
"Доктор Ватсон, иди сюда!"
,
},
{
"translatedText"
:
"Принеси мне кофе!"
,
}
]
}
Cloud Translation - Advanced API Python example
def
translate
():
response
=
translate_v3
.
TranslationServiceClient
()
.
translate_text
(
contents
=
[
"Life is short."
,
"Art is long."
],
target_language_code
=
'fr'
,
source_language_code
=
'en'
,
parent
=
f
"projects/
{
project_id
}
/locations/us-central1"
,
model
=
f
"
{
parent
}
/models/general/nmt"
)
print
(
response
)
return
response
translate
()
The output is a JSON document which looks like this:
translat
io
ns
{
translate
d_
te
x
t
:
"La vie est courte."
model
:
"projects/261347268520/locations/us-central1/models/general/nmt"
}
translat
io
ns
{
translate
d_
te
x
t
:
"L'art est long."
model
:
"projects/261347268520/locations/us-central1/models/general/nmt"
}
