Source code for sparknlp.annotator.seq2seq.m2m100_transformer
# Copyright 2017-2024 John Snow Labs
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"""Contains classes for the M2M100Transformer."""
from sparknlp.common import *
[docs]class M2M100Transformer(AnnotatorModel, HasBatchedAnnotate, HasEngine):
"""M2M100 : multilingual translation model
M2M100 is a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many
multilingual translation.
The model can directly translate between the 9,900 directions of 100 languages.
Pretrained models can be loaded with :meth:`.pretrained` of the companion
object:
>>> m2m100 = M2M100Transformer.pretrained() \\
... .setInputCols(["document"]) \\
... .setOutputCol("generation")
The default model is ``"m2m100_418M"``, if no name is provided. For available
pretrained models please see the `Models Hub
<https://sparknlp.org/models?q=m2m100>`__.
====================== ======================
Input Annotation types Output Annotation type
====================== ======================
``DOCUMENT`` ``DOCUMENT``
====================== ======================
Parameters
----------
configProtoBytes
ConfigProto from tensorflow, serialized into byte array.
minOutputLength
Minimum length of the sequence to be generated, by default 0
maxOutputLength
Maximum length of output text, by default 20
doSample
Whether or not to use sampling; use greedy decoding otherwise, by default False
temperature
The value used to module the next token probabilities, by default 1.0
topK
The number of highest probability vocabulary tokens to keep for
top-k-filtering, by default 50
topP
Top cumulative probability for vocabulary tokens, by default 1.0
If set to float < 1, only the most probable tokens with probabilities
that add up to ``topP`` or higher are kept for generation.
repetitionPenalty
The parameter for repetition penalty, 1.0 means no penalty. , by default
1.0
noRepeatNgramSize
If set to int > 0, all ngrams of that size can only occur once, by
default 0
ignoreTokenIds
A list of token ids which are ignored in the decoder's output, by
default []
srcLang
Source Language (Default: `en`)
tgtLang
Target Language (Default: `fr`)
Languages Covered
-----
Afrikaans (af), Amharic (am), Arabic (ar), Asturian (ast), Azerbaijani (az), Bashkir (ba),
Belarusian (be), Bulgarian (bg), Bengali (bn), Breton (br), Bosnian (bs), Catalan; Valencian
(ca), Cebuano (ceb), Czech (cs), Welsh (cy), Danish (da), German (de), Greeek (el), English
(en), Spanish (es), Estonian (et), Persian (fa), Fulah (ff), Finnish (fi), French (fr),
Western Frisian (fy), Irish (ga), Gaelic; Scottish Gaelic (gd), Galician (gl), Gujarati (gu),
Hausa (ha), Hebrew (he), Hindi (hi), Croatian (hr), Haitian; Haitian Creole (ht), Hungarian
(hu), Armenian (hy), Indonesian (id), Igbo (ig), Iloko (ilo), Icelandic (is), Italian (it),
Japanese (ja), Javanese (jv), Georgian (ka), Kazakh (kk), Central Khmer (km), Kannada (kn),
Korean (ko), Luxembourgish; Letzeburgesch (lb), Ganda (lg), Lingala (ln), Lao (lo), Lithuanian
(lt), Latvian (lv), Malagasy (mg), Macedonian (mk), Malayalam (ml), Mongolian (mn), Marathi
(mr), Malay (ms), Burmese (my), Nepali (ne), Dutch; Flemish (nl), Norwegian (no), Northern
Sotho (ns), Occitan (post 1500) (oc), Oriya (or), Panjabi; Punjabi (pa), Polish (pl), Pushto;
Pashto (ps), Portuguese (pt), Romanian; Moldavian; Moldovan (ro), Russian (ru), Sindhi (sd),
Sinhala; Sinhalese (si), Slovak (sk), Slovenian (sl), Somali (so), Albanian (sq), Serbian
(sr), Swati (ss), Sundanese (su), Swedish (sv), Swahili (sw), Tamil (ta), Thai (th), Tagalog
(tl), Tswana (tn), Turkish (tr), Ukrainian (uk), Urdu (ur), Uzbek (uz), Vietnamese (vi), Wolof
(wo), Xhosa (xh), Yiddish (yi), Yoruba (yo), Chinese (zh), Zulu (zu)
References
----------
- `Beyond English-Centric Multilingual Machine Translation
<https://arxiv.org/pdf/2010.11125.pdf>`__
- https://github.com/pytorch/fairseq/tree/master/examples/m2m_100
**Paper Abstract:**
* Existing work in translation demonstrated the potential of massively multilingual machine translation by training
a single model able to translate between any pair of languages. However, much of this work is English-Centric by
training only on data which was translated from or to English. While this is supported by large sources of
training data, it does not reflect translation needs worldwide. In this work, we create a true Many-to-Many
multilingual translation model that can translate directly between any pair of 100 languages. We build and open
source a training dataset that covers thousands of language directions with supervised data, created through
large-scale mining. Then, we explore how to effectively increase model capacity through a combination of dense
scaling and language-specific sparse parameters to create high quality models. Our focus on non-English-Centric
models brings gains of more than 10 BLEU when directly translating between non-English directions while performing
competitively to the best single systems of WMT. We open-source our scripts so that others may reproduce the data,
evaluation, and final M2M-100 model.*
Examples
--------
>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> documentAssembler = DocumentAssembler() \\
... .setInputCol("text") \\
... .setOutputCol("documents")
>>> m2m100 = M2M100Transformer.pretrained("m2m100_418M") \\
... .setInputCols(["documents"]) \\
... .setMaxOutputLength(50) \\
... .setOutputCol("generation") \\
... .setSrcLang("en") \\
... .setTgtLang("fr")
>>> pipeline = Pipeline().setStages([documentAssembler, m2m100])
>>> data = spark.createDataFrame([["生活就像一盒巧克力。"]]).toDF("text")
>>> result = pipeline.fit(data).transform(data)
>>> result.select("summaries.generation").show(truncate=False)
+-------------------------------------------------------------------------------------------+
|result |
+-------------------------------------------------------------------------------------------+
|[ Life is like a box of chocolate.] |
+-------------------------------------------------------------------------------------------+
"""
name = "M2M100Transformer"
inputAnnotatorTypes = [AnnotatorType.DOCUMENT]
outputAnnotatorType = AnnotatorType.DOCUMENT
configProtoBytes = Param(Params._dummy(), "configProtoBytes",
"ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()",
TypeConverters.toListInt)
minOutputLength = Param(Params._dummy(), "minOutputLength", "Minimum length of the sequence to be generated",
typeConverter=TypeConverters.toInt)
maxOutputLength = Param(Params._dummy(), "maxOutputLength", "Maximum length of output text",
typeConverter=TypeConverters.toInt)
doSample = Param(Params._dummy(), "doSample", "Whether or not to use sampling; use greedy decoding otherwise",
typeConverter=TypeConverters.toBoolean)
temperature = Param(Params._dummy(), "temperature", "The value used to module the next token probabilities",
typeConverter=TypeConverters.toFloat)
topK = Param(Params._dummy(), "topK",
"The number of highest probability vocabulary tokens to keep for top-k-filtering",
typeConverter=TypeConverters.toInt)
topP = Param(Params._dummy(), "topP",
"If set to float < 1, only the most probable tokens with probabilities that add up to ``top_p`` or higher are kept for generation",
typeConverter=TypeConverters.toFloat)
repetitionPenalty = Param(Params._dummy(), "repetitionPenalty",
"The parameter for repetition penalty. 1.0 means no penalty. See `this paper <https://arxiv.org/pdf/1909.05858.pdf>`__ for more details",
typeConverter=TypeConverters.toFloat)
noRepeatNgramSize = Param(Params._dummy(), "noRepeatNgramSize",
"If set to int > 0, all ngrams of that size can only occur once",
typeConverter=TypeConverters.toInt)
ignoreTokenIds = Param(Params._dummy(), "ignoreTokenIds",
"A list of token ids which are ignored in the decoder's output",
typeConverter=TypeConverters.toListInt)
beamSize = Param(Params._dummy(), "beamSize",
"The Number of beams for beam search.",
typeConverter=TypeConverters.toInt)
srcLang = Param(Params._dummy(), "srcLang", "Source Language (Default: `en`)",
typeConverter=TypeConverters.toString)
tgtLang = Param(Params._dummy(), "tgtLang", "Target Language (Default: `fr`)",
typeConverter=TypeConverters.toString)
[docs] def setIgnoreTokenIds(self, value):
"""A list of token ids which are ignored in the decoder's output.
Parameters
----------
value : List[int]
The words to be filtered out
"""
return self._set(ignoreTokenIds=value)
[docs] def setConfigProtoBytes(self, b):
"""Sets configProto from tensorflow, serialized into byte array.
Parameters
----------
b : List[int]
ConfigProto from tensorflow, serialized into byte array
"""
return self._set(configProtoBytes=b)
[docs] def setMinOutputLength(self, value):
"""Sets minimum length of the sequence to be generated.
Parameters
----------
value : int
Minimum length of the sequence to be generated
"""
return self._set(minOutputLength=value)
[docs] def setMaxOutputLength(self, value):
"""Sets maximum length of output text.
Parameters
----------
value : int
Maximum length of output text
"""
return self._set(maxOutputLength=value)
[docs] def setDoSample(self, value):
"""Sets whether or not to use sampling, use greedy decoding otherwise.
Parameters
----------
value : bool
Whether or not to use sampling; use greedy decoding otherwise
"""
return self._set(doSample=value)
[docs] def setTemperature(self, value):
"""Sets the value used to module the next token probabilities.
Parameters
----------
value : float
The value used to module the next token probabilities
"""
return self._set(temperature=value)
[docs] def setTopK(self, value):
"""Sets the number of highest probability vocabulary tokens to keep for
top-k-filtering.
Parameters
----------
value : int
Number of highest probability vocabulary tokens to keep
"""
return self._set(topK=value)
[docs] def setTopP(self, value):
"""Sets the top cumulative probability for vocabulary tokens.
If set to float < 1, only the most probable tokens with probabilities
that add up to ``topP`` or higher are kept for generation.
Parameters
----------
value : float
Cumulative probability for vocabulary tokens
"""
return self._set(topP=value)
[docs] def setRepetitionPenalty(self, value):
"""Sets the parameter for repetition penalty. 1.0 means no penalty.
Parameters
----------
value : float
The repetition penalty
References
----------
See `Ctrl: A Conditional Transformer Language Model For Controllable
Generation <https://arxiv.org/pdf/1909.05858.pdf>`__ for more details.
"""
return self._set(repetitionPenalty=value)
[docs] def setNoRepeatNgramSize(self, value):
"""Sets size of n-grams that can only occur once.
If set to int > 0, all ngrams of that size can only occur once.
Parameters
----------
value : int
N-gram size can only occur once
"""
return self._set(noRepeatNgramSize=value)
[docs] def setBeamSize(self, value):
"""Sets the number of beam size for beam search, by default `4`.
Parameters
----------
value : int
Number of beam size for beam search
"""
return self._set(beamSize=value)
[docs] def setSrcLang(self, value):
"""Sets source language.
Parameters
----------
value : str
Source language
"""
return self._set(srcLang=value)
[docs] def setTgtLang(self, value):
"""Sets target language.
Parameters
----------
value : str
Target language
"""
return self._set(tgtLang=value)
@keyword_only
def __init__(self, classname="com.johnsnowlabs.nlp.annotators.seq2seq.M2M100Transformer", java_model=None):
super(M2M100Transformer, self).__init__(classname=classname, java_model=java_model)
self._setDefault(minOutputLength=0,
maxOutputLength=200,
doSample=False,
temperature=1,
topK=50,
topP=1,
repetitionPenalty=1.0,
noRepeatNgramSize=0,
ignoreTokenIds=[],
batchSize=1,
beamSize=1,
srcLang="en",
tgtLang="fr")
@staticmethod
[docs] def loadSavedModel(folder, spark_session, use_openvino=False):
"""Loads a locally saved model.
Parameters
----------
folder : str
Folder of the saved model
spark_session : pyspark.sql.SparkSession
The current SparkSession
Returns
-------
M2M100Transformer
The restored model
"""
from sparknlp.internal import _M2M100Loader
jModel = _M2M100Loader(folder, spark_session._jsparkSession, use_openvino)._java_obj
return M2M100Transformer(java_model=jModel)
@staticmethod
[docs] def pretrained(name="m2m100_418M", lang="xx", remote_loc=None):
"""Downloads and loads a pretrained model.
Parameters
----------
name : str, optional
Name of the pretrained model, by default "m2m100_418M"
lang : str, optional
Language of the pretrained model, by default "en"
remote_loc : str, optional
Optional remote address of the resource, by default None. Will use
Spark NLPs repositories otherwise.
Returns
-------
M2M100Transformer
The restored model
"""
from sparknlp.pretrained import ResourceDownloader
return ResourceDownloader.downloadModel(M2M100Transformer, name, lang, remote_loc)