sparknlp.annotator.cv.phi3_vision_for_multimodal#

Classes#

class Phi3Vision(classname='com.johnsnowlabs.nlp.annotators.cv.Phi3Vision', java_model=None)[source]#

Phi3Vision can load Phi3Vision models for visual question answering. The model consists of a vision encoder, a text encoder as well as a text decoder. The vision encoder will encode the input image, the text encoder will encode the input question together with the encoding of the image, and the text decoder will output the answer to the question.

Pretrained models can be loaded with pretrained() of the companion object:

>>> visualQAClassifier = Phi3Vision.pretrained() \
...     .setInputCols(["image_assembler"]) \
...     .setOutputCol("answer")

The default model is "phi_3_vision_128k_instruct", if no name is provided.

For available pretrained models please see the Models Hub.

To see which models are compatible and how to import them see Import Transformers into Spark NLP 🚀.

Input Annotation types	Output Annotation type
IMAGE	DOCUMENT

Parameters:

batchSize

Batch size. Large values allows faster processing but requires more memory, by default 2

configProtoBytes

ConfigProto from tensorflow, serialized into byte array.

maxSentenceLength

Max sentence length to process, by default 50

Examples

>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> image_df = SparkSessionForTest.spark.read.format("image").load(path=images_path)
>>> test_df = image_df.withColumn("text", lit("<|user|> \n <|image_1|> \nWhat is unusual on this picture? <|end|>\n <|assistant|>\n"))
>>> imageAssembler = ImageAssembler() \
...     .setInputCol("image") \
...     .setOutputCol("image_assembler")
>>> visualQAClassifier = Phi3Vision.pretrained("phi_3_vision_128k_instruct","en") \
...     .setInputCols("image_assembler") \
...     .setOutputCol("answer")
>>> pipeline = Pipeline().setStages([
...     imageAssembler,
...     visualQAClassifier
... ])
>>> result = pipeline.fit(test_df).transform(test_df)
>>> result.select("image_assembler.origin", "answer.result").show(false)
+--------------------------------------+------+
|origin                                |result|
+--------------------------------------+------+
|[file:///content/images/cat_image.jpg]|[The unusual aspect of this picture is the presence of two cats lying on a pink couch]|
+--------------------------------------+------+

name = 'Phi3Vision'[source]#

inputAnnotatorTypes[source]#

outputAnnotatorType = 'document'[source]#

configProtoBytes[source]#

minOutputLength[source]#

maxOutputLength[source]#

doSample[source]#

temperature[source]#

topK[source]#

topP[source]#

repetitionPenalty[source]#

noRepeatNgramSize[source]#

ignoreTokenIds[source]#

beamSize[source]#

setMaxSentenceSize(value)[source]#

Sets Maximum sentence length that the annotator will process, by default 50.

Parameters:

valueint

Maximum sentence length that the annotator will process

setIgnoreTokenIds(value)[source]#

A list of token ids which are ignored in the decoder’s output.

Parameters:

valueList[int]

The words to be filtered out

setConfigProtoBytes(b)[source]#

Sets configProto from tensorflow, serialized into byte array.

Parameters:

bList[int]

ConfigProto from tensorflow, serialized into byte array

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, by default 4.

Parameters:

valueint

Number of beam size for beam search

static loadSavedModel(folder, spark_session, use_openvino=False)[source]#

Loads a locally saved model.

Parameters:

folderstr

Folder of the saved model

spark_sessionpyspark.sql.SparkSession

The current SparkSession

Returns:

CLIPForZeroShotClassification

The restored model

static pretrained(name='phi_3_vision_128k_instruct', lang='en', remote_loc=None)[source]#

Downloads and loads a pretrained model.

Parameters:

namestr, optional

Name of the pretrained model, by default “phi3v”

langstr, optional

Language of the pretrained model, by default “en”

remote_locstr, optional

Optional remote address of the resource, by default None. Will use Spark NLPs repositories otherwise.

Returns:

CLIPForZeroShotClassification

The restored model