TextEmbeddingModel(model_id: str, endpoint_name: typing.Optional[str] = None)
Creates a LanguageModel.
This constructor should not be called directly.
Use LanguageModel.from_pretrained(model_name=...)
instead.
Methods
batch_predict
batch_predict(
*,
dataset: typing.Union[str, typing.List[str]],
destination_uri_prefix: str,
model_parameters: typing.Optional[typing.Dict] = None
) -> google.cloud.aiplatform.jobs.BatchPredictionJob
Starts a batch prediction job with the model.
Exceptions | |
---|---|
Type | Description |
ValueError |
When source or destination URI is not supported. |
count_tokens
count_tokens(
prompts: typing.List[str],
) -> vertexai.preview.language_models.CountTokensResponse
Counts the tokens and billable characters for a given prompt.
Note: this does not make a prediction request to the model, it only counts the tokens in the request.
Parameter | |
---|---|
Name | Description |
prompts |
List[str]
Required. A list of prompts to ask the model. For example: ["What should I do today?", "How's it going?"] |
deploy_tuned_model
deploy_tuned_model(
tuned_model_name: str,
machine_type: typing.Optional[str] = None,
accelerator: typing.Optional[str] = None,
accelerator_count: typing.Optional[int] = None,
) -> vertexai.language_models._language_models._LanguageModel
Loads the specified tuned language model.
from_pretrained
from_pretrained(model_name: str) -> vertexai._model_garden._model_garden_models.T
Loads a _ModelGardenModel.
Exceptions | |
---|---|
Type | Description |
ValueError |
If model_name is unknown. |
ValueError |
If model does not support this class. |
get_embeddings
get_embeddings(
texts: typing.List[typing.Union[str, vertexai.language_models.TextEmbeddingInput]],
*,
auto_truncate: bool = True,
output_dimensionality: typing.Optional[int] = None
) -> typing.List[vertexai.language_models.TextEmbedding]
Calculates embeddings for the given texts.
get_embeddings_async
get_embeddings_async(
texts: typing.List[typing.Union[str, vertexai.language_models.TextEmbeddingInput]],
*,
auto_truncate: bool = True,
output_dimensionality: typing.Optional[int] = None
) -> typing.List[vertexai.language_models.TextEmbedding]
Asynchronously calculates embeddings for the given texts.
get_tuned_model
get_tuned_model(*args, **kwargs)
Loads the specified tuned language model.
list_tuned_model_names
list_tuned_model_names() -> typing.Sequence[str]
Lists the names of tuned models.
tune_model
tune_model(
*,
training_data: typing.Optional[str] = None,
corpus_data: typing.Optional[str] = None,
queries_data: typing.Optional[str] = None,
test_data: typing.Optional[str] = None,
validation_data: typing.Optional[str] = None,
batch_size: typing.Optional[int] = None,
train_steps: typing.Optional[int] = None,
tuned_model_location: typing.Optional[str] = None,
model_display_name: typing.Optional[str] = None,
task_type: typing.Optional[str] = None,
machine_type: typing.Optional[str] = None,
accelerator: typing.Optional[str] = None,
accelerator_count: typing.Optional[int] = None,
output_dimensionality: typing.Optional[int] = None,
learning_rate_multiplier: typing.Optional[float] = None
) -> vertexai.language_models._language_models._TextEmbeddingModelTuningJob
Tunes a model based on training data.
This method launches and returns an asynchronous model tuning job. Usage:
tuning_job = model.tune_model(...)
... do some other work
tuned_model = tuning_job.deploy_tuned_model() # Blocks until tuning is complete
Exceptions | |
---|---|
Type | Description |
ValueError |
If the provided parameter combinations or values are not supported. |
RuntimeError |
If the model does not support tuning |