Hi folks,
I am new to data science and AI as general.
I want to load a csv file that contains bus routes with columns as bus number, from, to ,start and end times all as strings.
How do i train this csv on a slm and ask basic questions on it . such as routes between a to b
by Hugging Chat.
To load your CSV file containing bus routes into the Hugging Face ecosystem and train a model to answer basic questions about routes (e.g., “What is the bus route between A and B?”), you can follow these steps:
First, you need to prepare your CSV file for training. The columns in your CSV file (bus number, from, to, start, and end times) should be formatted appropriately for the model to understand. For example, you can create input-output pairs where the input is a question and the output is the corresponding answer from your CSV file [1].
The Hugging Face Datasets library makes it easy to load and preprocess datasets. You can load your CSV file using the load_dataset function:
from datasets import load_dataset
# Load your CSV file
dataset = load_dataset("csv", data_files="bus_routes.csv")
You need to preprocess your data into a format suitable for a Question Answering (QA) task. For example, you can create questions and answers from your CSV data:
# Example preprocessing step
def preprocess_function(examples):
questions = []
answers = []
for i in range(len(examples["bus number"])):
# Create a question and answer pair
question = f"What is the bus route from {examples['from'][i]} to {examples['to'][i]}?"
answer = f"Bus {examples['bus number'][i]} operates from {examples['from'][i]} to {examples['to'][i]} starting at {examples['start'][i]} and ending at {examples['end'][i]}."
questions.append(question)
answers.append(answer)
return {"question": questions, "answer": answers}
# Preprocess the dataset
processed_dataset = dataset.map(preprocess_function)
Use a tokenizer from the Hugging Face Transformers library to tokenize your questions and answers. For example:
from transformers import AutoTokenizer
# Load a tokenizer (e.g., BERT tokenizer)
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
# Tokenize the dataset
tokenized_dataset = processed_dataset.map(
lambda examples: tokenizer(examples["question"], examples["answer"], truncation=True, padding=True),
batched=True,
)
Choose a pretrained model from the Hugging Face Model Hub (e.g., BERT, RoBERTa, or T5) and fine-tune it on your dataset:
from transformers import AutoModelForQuestionAnswering, TrainingArguments, Trainer
# Load a pretrained model
model = AutoModelForQuestionAnswering.from_pretrained("bert-base-uncased")
# Set up training arguments
training_args = TrainingArguments(
output_dir="bus_routes_qa",
learning_rate=2e-5,
num_train_epochs=3,
per_device_train_batch_size=16,
per_device_eval_batch_size=16,
logging_dir="logs",
)
# Initialize the trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_dataset["train"],
eval_dataset=tokenized_dataset["validation"],
)
# Train the model
trainer.train()
After training, evaluate your model and test it with sample questions to ensure it can answer correctly:
# Evaluate the model
trainer.evaluate()
# Test the model
from transformers import pipeline
qa_pipeline = pipeline("question-answering", model=model, tokenizer=tokenizer)
question = "What is the bus route from A to B?"
result = qa_pipeline(question=question, context="Bus routes data...") # Provide the context if needed
print(result)
Once your model is trained and evaluated, you can deploy it using the Hugging Face Hub. You can create a gradio demo or deploy it as an API using the Hugging Face Spaces platform [1].
You can now ask your model questions like “What is the bus route from A to B?” and it will provide answers based on your CSV data.
By following these steps, you can load your CSV file into the Hugging Face ecosystem, preprocess your data, fine-tune a pretrained model, and use it to answer basic questions about bus routes. The Hugging Face ecosystem provides powerful tools and libraries to make this process easier and more efficient [1][2].
Thanks for this John666. How do i prepare the dataset in a one to many relationship.
Example: List of buses between a and b
answer: …
by Hugging Chat again😅
To prepare the dataset and train a model to answer basic questions about bus routes, you can follow these steps. The goal is to create a model that can answer questions like “List of buses between A and B” or find the shortest route between two points.
Data Structure: Assume your CSV file has the following columns:
bus_number (string)from (string: starting station)to (string: ending station)start_time (string: time the bus departs from the starting station)end_time (string: time the bus arrives at the ending station)One-to-Many Relationship: Each bus can have multiple routes, and each route can involve multiple buses. For example, a route between stations A and B might involve Bus 1, Bus 2, etc. You need to represent this in a structured way, such as:
bus_number | from | to | start_time | end_time
----------------------------------------------
Bus_001 | A | B | 08:00 | 09:00
Bus_001 | B | C | 09:10 | 10:00
Bus_002 | A | B | 08:15 | 09:15
This structure shows that Bus 001 travels from A to B and then from B to C, while Bus 002 also travels from A to B.
Data Preprocessing:
start_time and end_time to datetime objects for easier manipulation [4].bus_number, from, and to using techniques like one-hot encoding or label encoding.Example Question: “List of buses between A and B.”
Implementation Example:
def find_routes(from_station, to_station, graph):
# Use BFS to find the shortest path
# Return the list of buses and stops
pass