sparknlp.common.properties#

Classes#

class HasBatchedAnnotate[source]#

batchSize[source]#

setBatchSize(v)[source]#

Sets batch size.

Parameters:

vint

Batch size

getBatchSize()[source]#

Gets current batch size.

Returns:

int

Current batch size

class HasCaseSensitiveProperties[source]#

caseSensitive[source]#

setCaseSensitive(value)[source]#

Sets whether to ignore case in tokens for embeddings matching.

Parameters:

valuebool

Whether to ignore case in tokens for embeddings matching

getCaseSensitive()[source]#

Gets whether to ignore case in tokens for embeddings matching.

Returns:

bool

Whether to ignore case in tokens for embeddings matching

class HasClsTokenProperties[source]#

useCLSToken[source]#

setUseCLSToken(value)[source]#

Sets whether to ignore case in tokens for embeddings matching.

Parameters:

valuebool

Whether to use CLS token for pooling (true) or attention-based average pooling (false)

getUseCLSToken()[source]#

Gets whether to use CLS token for pooling (true) or attention-based average pooling (false)

Returns:

bool

Whether to use CLS token for pooling (true) or attention-based average pooling (false)

class HasClassifierActivationProperties[source]#

activation[source]#

multilabel[source]#

threshold[source]#

setActivation(value)[source]#

Sets whether to calculate logits via Softmax or Sigmoid. Default is Softmax

Parameters:

valuestr

Whether to calculate logits via Softmax or Sigmoid. Default is Softmax

getActivation()[source]#

Gets whether to calculate logits via Softmax or Sigmoid. Default is Softmax

Returns:

str

Whether to calculate logits via Softmax or Sigmoid. Default is Softmax

setMultilabel(value)[source]#

Set whether or not the result should be multi-class (the sum of all probabilities is 1.0) or

multi-label (each label has a probability between 0.0 to 1.0). Default is False i.e. multi-class

Parameters:

valuebool

Whether or not the result should be multi-class (the sum of all probabilities is 1.0) or multi-label (each label has a probability between 0.0 to 1.0). Default is False i.e. multi-class

getMultilabel()[source]#

Gets whether or not the result should be multi-class (the sum of all probabilities is 1.0) or

multi-label (each label has a probability between 0.0 to 1.0). Default is False i.e. multi-class

Parameters:

valuebool

Whether or not the result should be multi-class (the sum of all probabilities is 1.0) or multi-label (each label has a probability between 0.0 to 1.0). Default is False i.e. multi-class

setThreshold(value)[source]#

Set the threshold to determine which logits are considered to be positive or negative.

(Default: 0.5). The value should be between 0.0 and 1.0. Changing the threshold value will affect the resulting labels and can be used to adjust the balance between precision and recall in the classification process.

Parameters:

valuefloat

The threshold to determine which logits are considered to be positive or negative. (Default: 0.5). The value should be between 0.0 and 1.0. Changing the threshold value will affect the resulting labels and can be used to adjust the balance between precision and recall in the classification process.

class HasEmbeddingsProperties[source]#

Components that take parameters. This also provides an internal param map to store parameter values attached to the instance.

New in version 1.3.0.

dimension[source]#

setDimension(value)[source]#

Sets embeddings dimension.

Parameters:

valueint

Embeddings dimension

getDimension()[source]#

Gets embeddings dimension.

class HasEnableCachingProperties[source]#

enableCaching[source]#

setEnableCaching(value)[source]#

Sets whether to enable caching DataFrames or RDDs during the training

Parameters:

valuebool

Whether to enable caching DataFrames or RDDs during the training

getEnableCaching()[source]#

Gets whether to enable caching DataFrames or RDDs during the training

Returns:

bool

Whether to enable caching DataFrames or RDDs during the training

class HasBatchedAnnotateImage[source]#

batchSize[source]#

setBatchSize(v)[source]#

Sets batch size.

Parameters:

vint

Batch size

getBatchSize()[source]#

Gets current batch size.

Returns:

int

Current batch size

class HasImageFeatureProperties[source]#

doResize[source]#

doNormalize[source]#

featureExtractorType[source]#

imageMean[source]#

imageStd[source]#

resample[source]#

size[source]#

setDoResize(value)[source]#

Parameters:

valueBoolean

Whether to resize the input to a certain size

setDoNormalize(value)[source]#

Parameters:

valueBoolean

Whether to normalize the input with mean and standard deviation

setFeatureExtractorType(value)[source]#

Parameters:

valuestr

Name of model’s architecture for feature extraction

setImageStd(value)[source]#

Parameters:

valueList[float]

The sequence of standard deviations for each channel, to be used when normalizing images

setImageMean(value)[source]#

Parameters:

valueList[float]

The sequence of means for each channel, to be used when normalizing images

setResample(value)[source]#

Parameters:

valueint

Resampling filter for resizing. This can be one of PIL.Image.NEAREST, PIL.Image.BILINEAR or PIL.Image.BICUBIC. Only has an effect if do_resize is set to True.

setSize(value)[source]#

Parameters:

valueint

Resize the input to the given size. If a tuple is provided, it should be (width, height).

class HasRescaleFactor[source]#

doRescale[source]#

rescaleFactor[source]#

setDoRescale(value)[source]#

Sets Whether to rescale the image values by rescaleFactor, by default True.

Parameters:

valueBoolean

Whether to rescale the image values by rescaleFactor.

setRescaleFactor(value)[source]#

Sets Factor to scale the image values, by default 1/255.0.

Parameters:

valueBoolean

Whether to rescale the image values by rescaleFactor.

class HasBatchedAnnotateAudio[source]#

batchSize[source]#

setBatchSize(v)[source]#

Sets batch size.

Parameters:

vint

Batch size

getBatchSize()[source]#

Gets current batch size.

Returns:

int

Current batch size

class HasAudioFeatureProperties[source]#

doNormalize[source]#

returnAttentionMask[source]#

paddingSide[source]#

featureSize[source]#

samplingRate[source]#

paddingValue[source]#

setDoNormalize(value)[source]#

Parameters:

valueBoolean

Whether to normalize the input with mean and standard deviation

setReturnAttentionMask(value)[source]#

Parameters:

valueboolean

setPaddingSide(value)[source]#

Parameters:

valuestr

setFeatureSize(value)[source]#

Parameters:

valueint

setSamplingRate(value)[source]#

Parameters:

valueInt

setPaddingValue(value)[source]#

Parameters:

valuefloat

class HasEngine[source]#

engine[source]#

skip_preferred_engine = False[source]#

getEngine()[source]#

Returns:

str

Deep Learning engine used for this model”

classmethod pretrainedEngine(name: str = 'default', lang: str = 'en', remote_loc: str = None, engine='onnx')[source]#

Downloads and loads a pretrained model.

Parameters:

namestr, optional

The name of the pretrained model, by default “default”

langstr, optional

The language of the pretrained model, by default “en”

remote_locstr, optional

Remote location of the model, by default None

enginestr, optional

The Deep Learning engine used for this model, by default “onnx”

Returns:

AnnotatorModel

Pretrained model

class HasCandidateLabelsProperties[source]#

candidateLabels[source]#

contradictionIdParam[source]#

entailmentIdParam[source]#

setCandidateLabels(v)[source]#

Sets candidateLabels.

Parameters:

vlist[string]

candidateLabels

setContradictionIdParam(v)[source]#

Sets contradictionIdParam.

Parameters:

vint

contradictionIdParam

setEntailmentIdParam(v)[source]#

Sets entailmentIdParam.

Parameters:

vint

entailmentIdParam

class HasMaxSentenceLengthLimit[source]#

max_length_limit = 512[source]#

maxSentenceLength[source]#

setMaxSentenceLength(value)[source]#

Sets max sentence length to process.

Note that a maximum limit exists depending on the model. If you are working with long single sequences, consider splitting up the input first with another annotator e.g. SentenceDetector.

Parameters:

valueint

Max sentence length to process

getMaxSentenceLength()[source]#

Gets max sentence of the model.

Returns:

int

Max sentence length to process

class HasLongMaxSentenceLengthLimit[source]#

max_length_limit = 4096[source]#

class HasGeneratorProperties[source]#

task[source]#

minOutputLength[source]#

maxOutputLength[source]#

doSample[source]#

temperature[source]#

topK[source]#

topP[source]#

repetitionPenalty[source]#

noRepeatNgramSize[source]#

beamSize[source]#

nReturnSequences[source]#

setTask(value)[source]#

Sets the transformer’s task, e.g. summarize:.

Parameters:

valuestr

The transformer’s task

setMinOutputLength(value)[source]#

Sets minimum length of the sequence to be generated.

Parameters:

valueint

Minimum length of the sequence to be generated

setMaxOutputLength(value)[source]#

Sets maximum length of output text.

Parameters:

valueint

Maximum length of output text

setDoSample(value)[source]#

Sets whether or not to use sampling, use greedy decoding otherwise.

Parameters:

valuebool

Whether or not to use sampling; use greedy decoding otherwise

setTemperature(value)[source]#

Sets the value used to module the next token probabilities.

Parameters:

valuefloat

The value used to module the next token probabilities

setTopK(value)[source]#

Sets the number of highest probability vocabulary tokens to keep for top-k-filtering.

Parameters:

valueint

Number of highest probability vocabulary tokens to keep

setTopP(value)[source]#

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:

valuefloat

Cumulative probability for vocabulary tokens

setRepetitionPenalty(value)[source]#

Sets the parameter for repetition penalty. 1.0 means no penalty.

Parameters:

valuefloat

The repetition penalty

References

See Ctrl: A Conditional Transformer Language Model For Controllable Generation for more details.

setNoRepeatNgramSize(value)[source]#

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:

valueint

N-gram size can only occur once

setBeamSize(value)[source]#

Sets the number of beam size for beam search.

Parameters:

valueint

Number of beam size for beam search

setNReturnSequences(value)[source]#

Sets the number of sequences to return from the beam search.

Parameters:

valueint

Number of sequences to return

class HasLlamaCppProperties[source]#

nThreads[source]#

nThreadsBatch[source]#

nCtx[source]#

nBatch[source]#

nUbatch[source]#

nDraft[source]#

nGpuLayers[source]#

nGpuLayersDraft[source]#

gpuSplitMode[source]#

mainGpu[source]#

ropeFreqBase[source]#

ropeFreqScale[source]#

yarnExtFactor[source]#

yarnAttnFactor[source]#

yarnBetaFast[source]#

yarnBetaSlow[source]#

yarnOrigCtx[source]#

defragmentationThreshold[source]#

numaStrategy[source]#

ropeScalingType[source]#

poolingType[source]#

modelDraft[source]#

modelAlias[source]#

embedding[source]#

flashAttention[source]#

useMmap[source]#

useMlock[source]#

noKvOffload[source]#

systemPrompt[source]#

chatTemplate[source]#

logVerbosity[source]#

disableLog[source]#

reasoningBudget[source]#

inputPrefix[source]#

inputSuffix[source]#

cachePrompt[source]#

nPredict[source]#

topK[source]#

topP[source]#

minP[source]#

tfsZ[source]#

typicalP[source]#

temperature[source]#

dynamicTemperatureRange[source]#

dynamicTemperatureExponent[source]#

repeatLastN[source]#

repeatPenalty[source]#

frequencyPenalty[source]#

presencePenalty[source]#

miroStat[source]#

miroStatTau[source]#

miroStatEta[source]#

penalizeNl[source]#

nKeep[source]#

seed[source]#

nProbs[source]#

minKeep[source]#

grammar[source]#

penaltyPrompt[source]#

ignoreEos[source]#

disableTokenIds[source]#

stopStrings[source]#

samplers[source]#

useChatTemplate[source]#

setNThreads(nThreads: int)[source]#

Set the number of threads to use during generation

setNThreadsBatch(nThreadsBatch: int)[source]#

Set the number of threads to use during batch and prompt processing

setNCtx(nCtx: int)[source]#

Set the size of the prompt context

setNBatch(nBatch: int)[source]#

Set the logical batch size for prompt processing (must be >=32 to use BLAS)

setNUbatch(nUbatch: int)[source]#

Set the physical batch size for prompt processing (must be >=32 to use BLAS)

setNDraft(nDraft: int)[source]#

Set the number of tokens to draft for speculative decoding

setNGpuLayers(nGpuLayers: int)[source]#

Set the number of layers to store in VRAM (-1 - use default)

setNGpuLayersDraft(nGpuLayersDraft: int)[source]#

Set the number of layers to store in VRAM for the draft model (-1 - use default)

setGpuSplitMode(gpuSplitMode: str)[source]#

Set how to split the model across GPUs

setMainGpu(mainGpu: int)[source]#

Set the main GPU that is used for scratch and small tensors.

setRopeFreqBase(ropeFreqBase: float)[source]#

Set the RoPE base frequency, used by NTK-aware scaling

setRopeFreqScale(ropeFreqScale: float)[source]#

Set the RoPE frequency scaling factor, expands context by a factor of 1/N

setYarnExtFactor(yarnExtFactor: float)[source]#

Set the YaRN extrapolation mix factor

setYarnAttnFactor(yarnAttnFactor: float)[source]#

Set the YaRN scale sqrt(t) or attention magnitude

setYarnBetaFast(yarnBetaFast: float)[source]#

Set the YaRN low correction dim or beta

setYarnBetaSlow(yarnBetaSlow: float)[source]#

Set the YaRN high correction dim or alpha

setYarnOrigCtx(yarnOrigCtx: int)[source]#

Set the YaRN original context size of model

setDefragmentationThreshold(defragmentationThreshold: float)[source]#

Set the KV cache defragmentation threshold

setNumaStrategy(numaStrategy: str)[source]#

Set optimization strategies that help on some NUMA systems (if available)

Possible values:

• DISABLED: No NUMA optimizations

• DISTRIBUTE: spread execution evenly over all

• ISOLATE: only spawn threads on CPUs on the node that execution started on

• NUMA_CTL: use the CPU map provided by numactl

• MIRROR: Mirrors the model across NUMA nodes

setRopeScalingType(ropeScalingType: str)[source]#

Set the RoPE frequency scaling method, defaults to linear unless specified by the model.

Possible values:

• NONE: Don’t use any scaling

• LINEAR: Linear scaling

• YARN: YaRN RoPE scaling

setPoolingType(poolingType: str)[source]#

Set the pooling type for embeddings, use model default if unspecified

Possible values:

• MEAN: Mean Pooling

• CLS: CLS Pooling

• LAST: Last token pooling

• RANK: For reranked models

setModelDraft(modelDraft: str)[source]#

Set the draft model for speculative decoding

setModelAlias(modelAlias: str)[source]#

Set a model alias

setFlashAttention(flashAttention: bool)[source]#

Whether to enable Flash Attention

setUseMmap(useMmap: bool)[source]#

Whether to use memory-map model (faster load but may increase pageouts if not using mlock)

setUseMlock(useMlock: bool)[source]#

Whether to force the system to keep model in RAM rather than swapping or compressing

setNoKvOffload(noKvOffload: bool)[source]#

Whether to disable KV offload

setSystemPrompt(systemPrompt: str)[source]#

Set a system prompt to use

setChatTemplate(chatTemplate: str)[source]#

The chat template to use

setInputPrefix(inputPrefix: str)[source]#

Set the prompt to start generation with

setInputSuffix(inputSuffix: str)[source]#

Set a suffix for infilling

setCachePrompt(cachePrompt: bool)[source]#

Whether to remember the prompt to avoid reprocessing it

setNPredict(nPredict: int)[source]#

Set the number of tokens to predict

setTopK(topK: int)[source]#

Set top-k sampling

setTopP(topP: float)[source]#

Set top-p sampling

setMinP(minP: float)[source]#

Set min-p sampling

setTfsZ(tfsZ: float)[source]#

Set tail free sampling, parameter z

setTypicalP(typicalP: float)[source]#

Set locally typical sampling, parameter p

setTemperature(temperature: float)[source]#

Set the temperature

setDynamicTemperatureRange(dynamicTemperatureRange: float)[source]#

Set the dynamic temperature range

setDynamicTemperatureExponent(dynamicTemperatureExponent: float)[source]#

Set the dynamic temperature exponent

setRepeatLastN(repeatLastN: int)[source]#

Set the last n tokens to consider for penalties

setRepeatPenalty(repeatPenalty: float)[source]#

Set the penalty of repeated sequences of tokens

setFrequencyPenalty(frequencyPenalty: float)[source]#

Set the repetition alpha frequency penalty

setPresencePenalty(presencePenalty: float)[source]#

Set the repetition alpha presence penalty

setMiroStat(miroStat: str)[source]#

Set MiroStat sampling strategies.

setMiroStatTau(miroStatTau: float)[source]#

Set the MiroStat target entropy, parameter tau

setMiroStatEta(miroStatEta: float)[source]#

Set the MiroStat learning rate, parameter eta

setPenalizeNl(penalizeNl: bool)[source]#

Whether to penalize newline tokens

setNKeep(nKeep: int)[source]#

Set the number of tokens to keep from the initial prompt

setSeed(seed: int)[source]#

Set the RNG seed

setNProbs(nProbs: int)[source]#

Set the amount top tokens probabilities to output if greater than 0.

setMinKeep(minKeep: int)[source]#

Set the amount of tokens the samplers should return at least (0 = disabled)

setGrammar(grammar: str)[source]#

Set BNF-like grammar to constrain generations

setPenaltyPrompt(penaltyPrompt: str)[source]#

Override which part of the prompt is penalized for repetition.

setIgnoreEos(ignoreEos: bool)[source]#

Set whether to ignore end of stream token and continue generating (implies –logit-bias 2-inf)

setDisableTokenIds(disableTokenIds: List[int])[source]#

Set the token ids to disable in the completion

setStopStrings(stopStrings: List[str])[source]#

Set strings upon seeing which token generation is stopped

setSamplers(samplers: List[str])[source]#

Set which samplers to use for token generation in the given order

setUseChatTemplate(useChatTemplate: bool)[source]#

Set whether generate should apply a chat template

setNParallel(nParallel: int)[source]#

Sets the number of parallel processes for decoding. This is an alias for setBatchSize.

setLogVerbosity(logVerbosity: int)[source]#

Set the log verbosity level

setReasoningBudget(reasoningBudget: int)[source]#

Controls the amount of thinking allowed; currently only one of: -1 for unrestricted thinking budget, or 0 to disable thinking (default: -1)

setDisableLog(disableLog: bool)[source]#

Whether to disable logging

setTokenIdBias(tokenIdBias: Dict[int, float])[source]#

Set token id bias

setTokenBias(tokenBias: Dict[str, float])[source]#

Set token id bias

getMetadata()[source]#

Gets the metadata of the model