GitHub Repository: ibm/watson-machine-learning-samples
Path: blob/master/cpd5.2/notebooks/python_sdk/deployments/foundation_models/Use watsonx, and `granite-3-3-8b-instruct` to analyze car rental customer satisfaction from text.ipynb
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Kernel: watsonx-ai-samples-py-312

Use watsonx, and `ibm/granite-3-3-8b-instruct` to analyze car rental customer satisfaction from text

Disclaimers

Use only Projects and Spaces that are available in watsonx context.

Notebook content

This notebook contains the steps and code to demonstrate support of text sentiment analysis in watsonx. It introduces commands for data retrieval, model testing and scoring.

Some familiarity with Python is helpful. This notebook uses Python 3.12.

Learning goal

The goal of this notebook is to demonstrate how to use ibm/granite-3-3-8b-instruct model to analyze customer satisfaction from text.

This notebook contains the following parts:

Set up the environment

Before you use the sample code in this notebook, you must perform the following setup tasks:

Contact with your Cloud Pak for Data administrator and ask them for your account credentials

Install dependencies

Note: ibm-watsonx-ai documentation can be found here.

In [1]:

%pip install -U wget | tail -n 1
%pip install -U datasets | tail -n 1
%pip install -U "scikit-learn==1.6.1" | tail -n 1
%pip install -U ibm-watsonx-ai | tail -n 1

Out[1]:

Successfully installed wget-3.2
Successfully installed aiohappyeyeballs-2.6.1 aiohttp-3.11.18 aiosignal-1.3.2 attrs-25.3.0 certifi-2025.4.26 charset-normalizer-3.4.2 datasets-3.6.0 dill-0.3.8 filelock-3.18.0 frozenlist-1.6.0 fsspec-2025.3.0 hf-xet-1.1.0 huggingface-hub-0.31.1 idna-3.10 multidict-6.4.3 multiprocess-0.70.16 numpy-2.2.5 pandas-2.2.3 propcache-0.3.1 pyarrow-20.0.0 pytz-2025.2 pyyaml-6.0.2 requests-2.32.3 tqdm-4.67.1 typing-extensions-4.13.2 tzdata-2025.2 urllib3-2.4.0 xxhash-3.5.0 yarl-1.20.0
Successfully installed joblib-1.5.0 scikit-learn-1.6.1 scipy-1.15.3 threadpoolctl-3.6.0
Successfully installed anyio-4.9.0 h11-0.16.0 httpcore-1.0.9 httpx-0.28.1 ibm-cos-sdk-2.14.0 ibm-cos-sdk-core-2.14.0 ibm-cos-sdk-s3transfer-2.14.0 ibm-watsonx-ai-1.3.13 jmespath-1.0.1 lomond-0.3.3 requests-2.32.2 sniffio-1.3.1 tabulate-0.9.0

Define credentials

Authenticate the watsonx.ai Runtime service on IBM Cloud Pak for Data. You need to provide the admin's username and the platform url.

In [2]:

username = "PASTE YOUR USERNAME HERE"
url = "PASTE THE PLATFORM URL HERE"

Use the admin's api_key to authenticate watsonx.ai Runtime services:

In [ ]:

import getpass
from ibm_watsonx_ai import Credentials

credentials = Credentials(
    username=username,
    api_key=getpass.getpass("Enter your watsonx.ai API key and hit enter: "),
    url=url,
    instance_id="openshift",
    version="5.2",
)

Alternatively you can use the admin's password:

In [3]:

import getpass
from ibm_watsonx_ai import Credentials

if "credentials" not in locals() or not credentials.api_key:
    credentials = Credentials(
        username=username,
        password=getpass.getpass("Enter your watsonx.ai password and hit enter: "),
        url=url,
        instance_id="openshift",
        version="5.2",
    )

Working with projects

First of all, you need to create a project that will be used for your work. If you do not have a project created already, follow the steps below:

Open IBM Cloud Pak main page
Click all projects
Create an empty project
Copy project_id from url and paste it below

Action: Assign project ID below

In [4]:

import os

try:
    project_id = os.environ["PROJECT_ID"]
except KeyError:
    project_id = input("Please enter your project_id (hit enter): ")

Create `APIClient` instance

In [5]:

from ibm_watsonx_ai import APIClient

client = APIClient(credentials, project_id)

Data loading

Download the car_rental_training_data dataset. The dataset provides insight about customers opinions on car rental. It has a label that consists of values: unsatisfied, satisfied.

In [6]:

import wget
import pandas as pd

filename = "car_rental_training_data.csv"
url = "https://raw.githubusercontent.com/IBM/watsonx-ai-samples/master/cpd5.2/data/cars-4-you/car_rental_training_data.csv"

if not os.path.isfile(filename):
    wget.download(url, out=filename)

df = pd.read_csv("car_rental_training_data.csv", sep=";")
data = df[["Customer_Service", "Satisfaction"]]

Examine downloaded data.

In [7]:

data.head()

Out[7]:

Prepare train and test sets.

In [8]:

from sklearn.model_selection import train_test_split

train, test = train_test_split(data, test_size=0.2)
comments = list(test.Customer_Service)
satisfaction = list(test.Satisfaction)

Foundation Models on `watsonx.ai`

List available models

In [9]:

for model in client.foundation_models.TextModels:
    print(f"- {model}")

Out[9]:

- ibm/granite-3-3-8b-instruct

You need to specify model_id that will be used for inferencing:

In [10]:

model_id = client.foundation_models.TextModels.GRANITE_3_3_8B_INSTRUCT

Defining the model parameters

You might need to adjust model parameters for different models or tasks, to do so please refer to documentation.

In [11]:

from ibm_watsonx_ai.metanames import GenTextParamsMetaNames as GenParams
from ibm_watsonx_ai.foundation_models.utils.enums import DecodingMethods

parameters = {
    GenParams.MIN_NEW_TOKENS: 0,
    GenParams.MAX_NEW_TOKENS: 1,
    GenParams.DECODING_METHOD: DecodingMethods.GREEDY,
    GenParams.REPETITION_PENALTY: 1,
}

Initialize the model

Initialize the ModelInference class with previous set params.

In [12]:

from ibm_watsonx_ai.foundation_models import ModelInference

model = ModelInference(
    model_id=model_id, params=parameters, credentials=credentials, project_id=project_id
)

Model's details

In [13]:

model.get_details()

Out[13]:

{'model_id': 'ibm/granite-3-3-8b-instruct',
 'label': 'granite-3-3-8b-instruct',
 'provider': 'IBM',
 'source': 'IBM',
 'functions': [{'id': 'autoai_rag'},
  {'id': 'multilingual'},
  {'id': 'text_chat'},
  {'id': 'text_generation'}],
 'short_description': 'Granite-3.3-8b-Instruct is an IBM-trained, dense decoder-only models, which is particularly well-suited for generative tasks.',
 'long_description': 'Granite-3.3-8b-Instruct is designed to be used for a wide range of generative and non-generative tasks with appropriate prompt engineering. It employs a GPT-style decoder-only architecture, with additional innovations from IBM Research and the open community.',
 'input_tier': 'class_12',
 'output_tier': 'class_12',
 'number_params': '8b',
 'min_shot_size': 1,
 'task_ids': ['question_answering',
  'summarization',
  'retrieval_augmented_generation',
  'classification',
  'generation',
  'code',
  'extraction',
  'translation',
  'function_calling'],
 'tasks': [{'id': 'question_answering'},
  {'id': 'summarization'},
  {'id': 'retrieval_augmented_generation'},
  {'id': 'classification'},
  {'id': 'generation'},
  {'id': 'code'},
  {'id': 'extraction'},
  {'id': 'translation'},
  {'id': 'function_calling'}],
 'model_limits': {'max_sequence_length': 131072, 'max_output_tokens': 16384},
 'limits': {'cpd': {'max_output_tokens': 16384}},
 'lifecycle': [{'id': 'available',
   'since_version': '11.0.0',
   'current_state': True}],
 'versions': [{'version': '3.3.0', 'available_date': '0001-01-01'}],
 'supported_languages': ['en', 'de', 'fr', 'it', 'pt', 'hi', 'es', 'th']}

Analyze the satisfaction

Prepare prompt and generate text

In [14]:

instruction = """Determine if the customer was satisfied with the experience based on the comment. Return simple yes or no.
Comment:The car was broken. They couldn't find a replacement. I've waster over 2 hours.
Satisfied:no"""

In [15]:

prompt1 = "\n".join([instruction, "Comment:" + comments[2], "Satisfied:"])
print(prompt1)

Out[15]:

Determine if the customer was satisfied with the experience based on the comment. Return simple yes or no.
Comment:The car was broken. They couldn't find a replacement. I've waster over 2 hours.
Satisfied:no
Comment:Customer service was fine.  We didn't care for the off site location.  Would not use that car rental company at that airport again.
Satisfied:

Analyze the sentiment for a sample of zero-shot input from the test set.

In [16]:

print(model.generate_text(prompt=prompt1))

Out[16]:

no

Calculate the accuracy

In [17]:

sample_size = 10
prompts_batch = [
    "\n".join([instruction, "Comment:" + comment, "Satisfied:"])
    for comment in comments[:10]
]
results = model.generate_text(prompt=prompts_batch)

In [18]:

print(prompts_batch[0])

Out[18]:

Determine if the customer was satisfied with the experience based on the comment. Return simple yes or no.
Comment:The car was broken. They couldn't find a replacement. I've waster over 2 hours.
Satisfied:no
Comment:the rep was friendly but it was so loud in there that I could not hear what she was saying. I HATE having to walk across a big lot with all of my bags in search of my car which is always in the furthest corner.
Satisfied:

Score the model

In [19]:

from sklearn.metrics import accuracy_score

label_map = {0: "no", 1: "yes"}
y_true = [label_map[sat] for sat in satisfaction][:sample_size]

print("accuracy_score", accuracy_score(y_true, results))

Out[19]:

accuracy_score 0.9

In [20]:

print("true", y_true)
print("pred", results)

Out[20]:

true ['no', 'no', 'no', 'yes', 'yes', 'no', 'yes', 'no', 'no', 'no']
pred ['no', 'no', 'no', 'yes', 'yes', 'yes', 'yes', 'no', 'no', 'no']

Summary and next steps

You successfully completed this notebook!

You learned how to analyze car rental customer satisfaction with watsonx.ai foundation model.

Check out our Online Documentation for more samples, tutorials, documentation, how-tos, and blog posts.

Authors

Mateusz Szewczyk, Software Engineer at watsonx.ai.

Lukasz Cmielowski, PhD, is an Automation Architect and Data Scientist at IBM with a track record of developing enterprise-level applications that substantially increases clients' ability to turn data into actionable knowledge.

Use watsonx, and `ibm/granite-3-3-8b-instruct` to analyze car rental customer satisfaction from text

Disclaimers

Notebook content

Learning goal

Contents

Set up the environment

Install dependencies

Define credentials

Working with projects

Create `APIClient` instance

Data loading

Foundation Models on `watsonx.ai`

List available models

Defining the model parameters

Initialize the model

Model's details

Analyze the satisfaction

Prepare prompt and generate text

Calculate the accuracy

Score the model

Summary and next steps

Authors

Product

Resources

Company

Use watsonx, and ibm/granite-3-3-8b-instruct to analyze car rental customer satisfaction from text

Disclaimers

Notebook content

Learning goal

Contents

Set up the environment

Install dependencies

Define credentials

Working with projects

Create APIClient instance

Data loading

Foundation Models on watsonx.ai

List available models

Defining the model parameters

Initialize the model

Model's details

Analyze the satisfaction

Prepare prompt and generate text

Calculate the accuracy

Score the model

Summary and next steps

Authors

Use watsonx, and `ibm/granite-3-3-8b-instruct` to analyze car rental customer satisfaction from text

Create `APIClient` instance

Foundation Models on `watsonx.ai`