Source code for sparknlp.annotator.seq2seq.cohere_transformer
# Copyright 2017-2022 John Snow Labs
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"""Contains classes for the CoHereTransformer."""
from sparknlp.common import *
[docs]class CoHereTransformer(AnnotatorModel, HasBatchedAnnotate, HasEngine):
"""Cohere: Command-R Transformer
C4AI Command-R is a research release of a 35 billion parameter highly performant generative model.
Command-R is a large language model with open weights optimized for a variety of use cases including reasoning,
summarization, and question answering. Command-R has the capability for multilingual generation evaluated
in 10 languages and highly performant RAG capabilities.
Pretrained models can be loaded with :meth:`.pretrained` of the companion
object:
>>> CoHere = CoHereTransformer.pretrained() \\
... .setInputCols(["document"]) \\
... .setOutputCol("generation")
The default model is ``"c4ai_command_r_v01_int4"``, if no name is provided. For available
pretrained models please see the `Models Hub
<https://sparknlp.org/models?q=CoHere>`__.
====================== ======================
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 60
doSample
Whether or not to use sampling; use greedy decoding otherwise, by default False
temperature
The value used to modulate the next token probabilities, by default 1.0
topK
The number of highest probability vocabulary tokens to keep for
top-k-filtering, by default 40
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 []
Notes
-----
This is a very computationally expensive module, especially on larger
sequences. The use of an accelerator such as GPU is recommended.
References
----------
- `Cohere <https://cohere.for.ai/>`__
Examples
--------
>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> documentAssembler = DocumentAssembler() \\
... .setInputCol("text") \\
... .setOutputCol("documents")
>>> CoHere = CoHereTransformer.pretrained("c4ai_command_r_v01_int4","en") \\
... .setInputCols(["documents"]) \\
... .setMaxOutputLength(60) \\
... .setOutputCol("generation")
>>> pipeline = Pipeline().setStages([documentAssembler, CoHere])
>>> data = spark.createDataFrame([
... (
... 1,
... "<BOS_TOKEN><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Hello, how are you?<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>"
... )
... ]).toDF("id", "text")
>>> result = pipeline.fit(data).transform(data)
>>> result.select("generation.result").show(truncate=False)
+------------------------------------------------+
|result |
+------------------------------------------------+
|[Hello! I'm doing well, thank you for asking! I'm excited to help you with whatever questions you have today. How can I assist you?]|
+------------------------------------------------+
"""
name = "CoHereTransformer"
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 to use for beam search",
typeConverter=TypeConverters.toInt)
stopTokenIds = Param(Params._dummy(), "stopTokenIds",
"A list of token ids which are considered as stop tokens in the decoder's output",
typeConverter=TypeConverters.toListInt)
[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 beams to use for beam search.
Parameters
----------
value : int
The number of beams to use for beam search
"""
return self._set(beamSize=value)
[docs] def setStopTokenIds(self, value):
"""Sets a list of token ids which are considered as stop tokens in the decoder's output.
Parameters
----------
value : List[int]
The words to be considered as stop tokens
"""
return self._set(stopTokenIds=value)
@keyword_only
def __init__(self, classname="com.johnsnowlabs.nlp.annotators.seq2seq.CoHereTransformer", java_model=None):
super(CoHereTransformer, self).__init__(
classname=classname,
java_model=java_model
)
self._setDefault(
minOutputLength=0,
maxOutputLength=20,
doSample=False,
temperature=0.6,
topK=-1,
topP=0.9,
repetitionPenalty=1.0,
noRepeatNgramSize=3,
ignoreTokenIds=[],
batchSize=1,
beamSize=1,
stopTokenIds=[128001, ]
)
[docs] @staticmethod
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
-------
CoHereTransformer
The restored model
"""
from sparknlp.internal import _CoHereLoader
jModel = _CoHereLoader(folder, spark_session._jsparkSession, use_openvino)._java_obj
return CoHereTransformer(java_model=jModel)
[docs] @staticmethod
def pretrained(name="c4ai_command_r_v01_int4", lang="en", remote_loc=None):
"""Downloads and loads a pretrained model.
Parameters
----------
name : str, optional
Name of the pretrained model, by default "c4ai_command_r_v01_int4"
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
-------
CoHereTransformer
The restored model
"""
from sparknlp.pretrained import ResourceDownloader
return ResourceDownloader.downloadModel(CoHereTransformer, name, lang, remote_loc)