Module language_models (1.95.1)

Classes for working with language models.

Classes

ChatMessage

ChatMessage(content: str, author: str)

A chat message.

CountTokensResponse

CountTokensResponse(
    total_tokens: int,
    total_billable_characters: int,
    _count_tokens_response: typing.Any,
)

The response from a count_tokens request. .. attribute:: total_tokens

The total number of tokens counted across all instances passed to the request.

:type: int

EvaluationClassificationMetric

EvaluationClassificationMetric(
    label_name: typing.Optional[str] = None,
    auPrc: typing.Optional[float] = None,
    auRoc: typing.Optional[float] = None,
    logLoss: typing.Optional[float] = None,
    confidenceMetrics: typing.Optional[
        typing.List[typing.Dict[str, typing.Any]]
    ] = None,
    confusionMatrix: typing.Optional[typing.Dict[str, typing.Any]] = None,
)

The evaluation metric response for classification metrics.

Parameters
Name Description
label_name str

Optional. The name of the label associated with the metrics. This is only returned when only_summary_metrics=False is passed to evaluate().
auPrc float

Optional. The area under the precision recall curve.
auRoc float

Optional. The area under the receiver operating characteristic curve.
logLoss float

Optional. Logarithmic loss.
confidenceMetrics List[Dict[str, Any]]

Optional. This is only returned when only_summary_metrics=False is passed to evaluate().
confusionMatrix Dict[str, Any]

Optional. This is only returned when only_summary_metrics=False is passed to evaluate().

EvaluationMetric

EvaluationMetric(
    bleu: typing.Optional[float] = None, rougeLSum: typing.Optional[float] = None
)

The evaluation metric response.

Parameters
Name Description
bleu float

Optional. BLEU (Bilingual evauation understudy). Scores based on sacrebleu implementation.
rougeLSum float

Optional. ROUGE-L (Longest Common Subsequence) scoring at summary level.

EvaluationQuestionAnsweringSpec

EvaluationQuestionAnsweringSpec(
    ground_truth_data: typing.Union[typing.List[str], str, pandas.core.frame.DataFrame],
    task_name: str = "question-answering",
)

Spec for question answering model evaluation tasks.

EvaluationTextClassificationSpec

EvaluationTextClassificationSpec(
    ground_truth_data: typing.Union[typing.List[str], str, pandas.core.frame.DataFrame],
    target_column_name: str,
    class_names: typing.List[str],
)

Spec for text classification model evaluation tasks.

Parameters
Name Description
target_column_name str

Required. The label column in the dataset provided in ground_truth_data. Required when task_name='text-classification'.
class_names List[str]

Required. A list of all possible label names in your dataset. Required when task_name='text-classification'.

EvaluationTextGenerationSpec

EvaluationTextGenerationSpec(
    ground_truth_data: typing.Union[typing.List[str], str, pandas.core.frame.DataFrame],
)

Spec for text generation model evaluation tasks.

EvaluationTextSummarizationSpec

EvaluationTextSummarizationSpec(
    ground_truth_data: typing.Union[typing.List[str], str, pandas.core.frame.DataFrame],
    task_name: str = "summarization",
)

Spec for text summarization model evaluation tasks.

InputOutputTextPair

InputOutputTextPair(input_text: str, output_text: str)

InputOutputTextPair represents a pair of input and output texts.

TextEmbedding

TextEmbedding(
    values: typing.List[float],
    statistics: typing.Optional[
        vertexai.language_models.TextEmbeddingStatistics
    ] = None,
    _prediction_response: typing.Optional[
        google.cloud.aiplatform.models.Prediction
    ] = None,
)

Text embedding vector and statistics.

TextEmbeddingInput

TextEmbeddingInput(
    text: str,
    task_type: typing.Optional[str] = None,
    title: typing.Optional[str] = None,
)

Structural text embedding input.

TextGenerationResponse

TextGenerationResponse(text: str, _prediction_response: typing.Any, is_blocked: bool = False, errors: typing.Tuple[int] = (), safety_attributes: typing.Dict[str, float] = <factory>, grounding_metadata: typing.Optional[vertexai.language_models._language_models.GroundingMetadata] = None)

TextGenerationResponse represents a response of a language model. .. attribute:: text

The generated text

:type: str

TuningEvaluationSpec

TuningEvaluationSpec(
    evaluation_data: typing.Optional[str] = None,
    evaluation_interval: typing.Optional[int] = None,
    enable_early_stopping: typing.Optional[bool] = None,
    enable_checkpoint_selection: typing.Optional[bool] = None,
    tensorboard: typing.Optional[
        typing.Union[
            google.cloud.aiplatform.tensorboard.tensorboard_resource.Tensorboard, str
        ]
    ] = None,
)

Specification for model evaluation to perform during tuning.