sparknlp.annotator.seq2seq.mistral_transformer#

Classes#

class MistralTransformer(classname='com.johnsnowlabs.nlp.annotators.seq2seq.MistralTransformer', java_model=None)[source]#

Mistral 7B

Mistral 7B, a 7.3 billion-parameter model that stands out for its efficient and effective performance in natural language processing. Surpassing Llama 2 13B across all benchmarks and excelling over Llama 1 34B in various aspects, Mistral 7B strikes a balance between English language tasks and code comprehension, rivaling the capabilities of CodeLlama 7B in the latter.

Mistral 7B introduces Grouped-query attention (GQA) for quicker inference, enhancing processing speed without compromising accuracy. This streamlined approach ensures a smoother user experience, making Mistral 7B a practical choice for real-world applications.

Additionally, Mistral 7B adopts Sliding Window Attention (SWA) to efficiently handle longer sequences at a reduced computational cost. This feature enhances the model’s ability to process extensive textual input, expanding its utility in handling more complex tasks.

In summary, Mistral 7B represents a notable advancement in language models, offering a reliable and versatile solution for various natural language processing challenges.

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

>>> mistral = MistralTransformer.pretrained() \
...     .setInputCols(["document"]) \
...     .setOutputCol("generation")

The default model is "mistral_7b", if no name is provided. For available pretrained models please see the Models Hub.

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 []

Notes

This is a very computationally expensive module especially on larger sequence. The use of an accelerator such as GPU is recommended.

Question: Leonardo Da Vinci invented the microscope? Answer: No, Leonardo Da Vinci did not invent the microscope. The first microscope was invented |

in the late 16th century, long after Leonardo’] |

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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

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:

MistralTransformer

The restored model

static pretrained(name='mistral_7b', lang='en', remote_loc=None)[source]#

Downloads and loads a pretrained model.

Parameters:

namestr, optional

Name of the pretrained model, by default “mistral_7b”

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:

MistralTransformer

The restored model