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GeminiTextGenerator(
*,
model_name: typing.Literal[
"gemini-pro",
"gemini-1.5-pro-preview-0514",
"gemini-1.5-flash-preview-0514",
"gemini-1.5-pro-001",
"gemini-1.5-pro-002",
"gemini-1.5-flash-001",
"gemini-1.5-flash-002",
] = "gemini-pro",
session: typing.Optional[bigframes.session.Session] = None,
connection_name: typing.Optional[str] = None,
max_iterations: int = 300
)
Gemini text generator LLM model.
Parameters |
|
---|---|
Name | Description |
model_name |
str, Default to "gemini-pro"
The model for natural language tasks. Accepted values are "gemini-pro", "gemini-1.5-pro-preview-0514", "gemini-1.5-flash-preview-0514", "gemini-1.5-pro-001", "gemini-1.5-pro-002", "gemini-1.5-flash-001" and "gemini-1.5-flash-002". Default to "gemini-pro". |
session |
bigframes.Session or None
BQ session to create the model. If None, use the global default session. |
connection_name |
str or None
Connection to connect with remote service. str of the format <PROJECT_NUMBER/PROJECT_ID>.
|
max_iterations |
Optional[int], Default to 300
The number of steps to run when performing supervised tuning. |
Methods
__repr__
__repr__()
Print the estimator's constructor with all non-default parameter values.
fit
fit(
X: typing.Union[
bigframes.dataframe.DataFrame,
bigframes.series.Series,
pandas.core.frame.DataFrame,
pandas.core.series.Series,
],
y: typing.Union[
bigframes.dataframe.DataFrame,
bigframes.series.Series,
pandas.core.frame.DataFrame,
pandas.core.series.Series,
],
) -> bigframes.ml.llm.GeminiTextGenerator
Fine tune GeminiTextGenerator model. Only support "gemini-pro" model for now.
Parameter | |
---|---|
Name | Description |
X |
bigframes.dataframe.DataFrame or bigframes.series.Series
DataFrame of shape (n_samples, n_features). Training data. |
Returns | |
---|---|
Type | Description |
GeminiTextGenerator |
Fitted estimator. |
get_params
get_params(deep: bool = True) -> typing.Dict[str, typing.Any]
Get parameters for this estimator.
Parameter | |
---|---|
Name | Description |
deep |
bool, default True
Default |
Returns | |
---|---|
Type | Description |
Dictionary |
A dictionary of parameter names mapped to their values. |
predict
predict(
X: typing.Union[
bigframes.dataframe.DataFrame,
bigframes.series.Series,
pandas.core.frame.DataFrame,
pandas.core.series.Series,
],
*,
temperature: float = 0.9,
max_output_tokens: int = 8192,
top_k: int = 40,
top_p: float = 1.0
) -> bigframes.dataframe.DataFrame
Predict the result from input DataFrame.
Parameters | |
---|---|
Name | Description |
X |
bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series
Input DataFrame or Series, can contain one or more columns. If multiple columns are in the DataFrame, it must contain a "prompt" column for prediction. Prompts can include preamble, questions, suggestions, instructions, or examples. |
temperature |
float, default 0.9
The temperature is used for sampling during the response generation, which occurs when topP and topK are applied. Temperature controls the degree of randomness in token selection. Lower temperatures are good for prompts that require a more deterministic and less open-ended or creative response, while higher temperatures can lead to more diverse or creative results. A temperature of 0 is deterministic: the highest probability response is always selected. Default 0.9. Possible values [0.0, 1.0]. |
max_output_tokens |
int, default 8192
Maximum number of tokens that can be generated in the response. A token is approximately four characters. 100 tokens correspond to roughly 60-80 words. Specify a lower value for shorter responses and a higher value for potentially longer responses. Default 8192. Possible values are in the range [1, 8192]. |
top_k |
int, default 40
Top-K changes how the model selects tokens for output. A top-K of 1 means the next selected token is the most probable among all tokens in the model's vocabulary (also called greedy decoding), while a top-K of 3 means that the next token is selected from among the three most probable tokens by using temperature. For each token selection step, the top-K tokens with the highest probabilities are sampled. Then tokens are further filtered based on top-P with the final token selected using temperature sampling. Specify a lower value for less random responses and a higher value for more random responses. Default 40. Possible values [1, 40]. |
top_p |
float, default 0.95
Top-P changes how the model selects tokens for output. Tokens are selected from the most (see top-K) to least probable until the sum of their probabilities equals the top-P value. For example, if tokens A, B, and C have a probability of 0.3, 0.2, and 0.1 and the top-P value is 0.5, then the model will select either A or B as the next token by using temperature and excludes C as a candidate. Specify a lower value for less random responses and a higher value for more random responses. Default 1.0. Possible values [0.0, 1.0]. |
Returns | |
---|---|
Type | Description |
bigframes.dataframe.DataFrame |
DataFrame of shape (n_samples, n_input_columns + n_prediction_columns). Returns predicted values. |
score
score(
X: typing.Union[
bigframes.dataframe.DataFrame,
bigframes.series.Series,
pandas.core.frame.DataFrame,
pandas.core.series.Series,
],
y: typing.Union[
bigframes.dataframe.DataFrame,
bigframes.series.Series,
pandas.core.frame.DataFrame,
pandas.core.series.Series,
],
task_type: typing.Literal[
"text_generation", "classification", "summarization", "question_answering"
] = "text_generation",
) -> bigframes.dataframe.DataFrame
Calculate evaluation metrics of the model. Only "gemini-pro" model is supported for now.
Output matches that of the BigQuery ML.EVALUATE function. See: https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-evaluate#remote-model-llm for the outputs relevant to this model type.Parameters | |
---|---|
Name | Description |
X |
bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series
A BigQuery DataFrame as evaluation data, which contains only one column of input_text that contains the prompt text to use when evaluating the model. |
y |
bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series
A BigQuery DataFrame as evaluation labels, which contains only one column of output_text that you would expect to be returned by the model. |
task_type |
str
The type of the task for LLM model. Default to "text_generation". Possible values: "text_generation", "classification", "summarization", and "question_answering". |
Returns | |
---|---|
Type | Description |
bigframes.dataframe.DataFrame |
The DataFrame as evaluation result. |
to_gbq
to_gbq(
model_name: str, replace: bool = False
) -> bigframes.ml.llm.GeminiTextGenerator
Save the model to BigQuery.
Parameters | |
---|---|
Name | Description |
model_name |
str
The name of the model. |
replace |
bool, default False
Determine whether to replace if the model already exists. Default to False. |
Returns | |
---|---|
Type | Description |
GeminiTextGenerator |
Saved model. |