summarization.py

# Copyright 2023 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os

PROJECT_ID = os.getenv("GOOGLE_CLOUD_PROJECT")


def text_summarization() -> str:
    """Summarization Example with a Large Language Model"""
    # [START aiplatform_sdk_summarization]
    import vertexai
    from vertexai.language_models import TextGenerationModel

    # TODO(developer): update project_id & location
    vertexai.init(project=PROJECT_ID, location="us-central1")

    parameters = {
        "temperature": 0,
        "max_output_tokens": 256,
        "top_p": 0.95,
        "top_k": 40,
    }

    model = TextGenerationModel.from_pretrained("text-bison@002")
    response = model.predict(
        """Provide a summary with about two sentences for the following article:
        The efficient-market hypothesis (EMH) is a hypothesis in financial \
        economics that states that asset prices reflect all available \
        information. A direct implication is that it is impossible to \
        "beat the market" consistently on a risk-adjusted basis since market \
        prices should only react to new information. Because the EMH is \
        formulated in terms of risk adjustment, it only makes testable \
        predictions when coupled with a particular model of risk. As a \
        result, research in financial economics since at least the 1990s has \
        focused on market anomalies, that is, deviations from specific \
        models of risk. The idea that financial market returns are difficult \
        to predict goes back to Bachelier, Mandelbrot, and Samuelson, but \
        is closely associated with Eugene Fama, in part due to his \
        influential 1970 review of the theoretical and empirical research. \
        The EMH provides the basic logic for modern risk-based theories of \
        asset prices, and frameworks such as consumption-based asset pricing \
        and intermediary asset pricing can be thought of as the combination \
        of a model of risk with the EMH. Many decades of empirical research \
        on return predictability has found mixed evidence. Research in the \
        1950s and 1960s often found a lack of predictability (e.g. Ball and \
        Brown 1968; Fama, Fisher, Jensen, and Roll 1969), yet the \
        1980s-2000s saw an explosion of discovered return predictors (e.g. \
        Rosenberg, Reid, and Lanstein 1985; Campbell and Shiller 1988; \
        Jegadeesh and Titman 1993). Since the 2010s, studies have often \
        found that return predictability has become more elusive, as \
        predictability fails to work out-of-sample (Goyal and Welch 2008), \
        or has been weakened by advances in trading technology and investor \
        learning (Chordia, Subrahmanyam, and Tong 2014; McLean and Pontiff \
        2016; Martineau 2021).
        Summary:""",
        **parameters,
    )
    print(f"Response from Model: {response.text}")
    # [END aiplatform_sdk_summarization]

    return response.text