sparknlp.annotator.cv.qwen2vl_transformer
#
Module Contents#
Classes#
Qwen2VLTransformer can load Qwen2 Vision-Language models for visual question answering |
- class Qwen2VLTransformer(classname='com.johnsnowlabs.nlp.annotators.cv.Qwen2VLTransformer', java_model=None)[source]#
Qwen2VLTransformer can load Qwen2 Vision-Language models for visual question answering and multimodal instruction following. The model consists of a vision encoder, a text encoder, and a text decoder. The vision encoder processes the input image, the text encoder integrates the encoding of the image with the input text, and the text decoder outputs the response to the query or instruction.
Pretrained models can be loaded with
pretrained()
of the companion object:>>> visualQAClassifier = Qwen2VLTransformer.pretrained() \ ... .setInputCols(["image_assembler"]) \ ... .setOutputCol("answer")
The default model is
"qwen2_vl_2b_instruct_int4"
, if no name is provided.For available pretrained models, please see the Models Hub.
Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. To see which models are compatible and how to import them, see Import Transformers into Spark NLP 🚀. For more extended examples, see Spark NLP Test Suite for Qwen2VLTransformer.
Input Annotation types
Output Annotation type
IMAGE
DOCUMENT
- Parameters:
- batchSize
Batch size. Large values allow faster processing but require 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("<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>Describe this image.<|im_end|>\n<|im_start|>assistant\n")) >>> imageAssembler = ImageAssembler() \ ... .setInputCol("image") \ ... .setOutputCol("image_assembler") >>> visualQAClassifier = Qwen2VLTransformer.pretrained() \ ... .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]|[This image is unusual because it features two cats lying on a pink couch.]| +--------------------------------------+------+
- 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='qwen2_vl_2b_instruct_int4', lang='en', remote_loc=None)[source]#
Downloads and loads a pretrained model.
- Parameters:
- namestr, optional
Name of the pretrained model, by default “qwen2_vl_2b_instruct_int4”
- 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:
- Qwen2VLTransformer
The restored model