Preparing a Dataset for Fine-Tuning Foundation Model

 

I am trying to preparing a Dataset for Fine-Tuning on Pathology Lab Data.

1. Dataset Collection

   - Sources: Gather data from pathology lab reports, medical journals, and any other relevant medical documents.

   - Format: Ensure that the data is in a readable format like CSV, JSON, or text files.

2. Data Preprocessing

   - Cleaning: Remove any irrelevant data, correct typos, and handle missing values.

   - Formatting: Convert the data into a format suitable for fine-tuning, usually pairs of input and output texts.

   - Example Format:

     - Input: "Patient exhibits symptoms of hyperglycemia."

     - Output: "Hyperglycemia"

3. Tokenization

   - Tokenize the text using the tokenizer that corresponds to the model you intend to fine-tune.

Example Code for Dataset Preparation

Using Pandas and Transformers for Preprocessing

1. Install Required Libraries:

   ```sh

   pip install pandas transformers datasets

   ```

2. Load and Clean the Data:

   ```python

   import pandas as pd

   # Load your dataset

   df = pd.read_csv("pathology_lab_data.csv")

   # Example: Remove rows with missing values

   df.dropna(inplace=True)

   # Select relevant columns (e.g., 'report' and 'diagnosis')

   df = df[['report', 'diagnosis']]

   ```

3. Tokenize the Data:

   ```python

   from transformers import AutoTokenizer

   model_name = "pretrained_model_name"

   tokenizer = AutoTokenizer.from_pretrained(model_name)

   def tokenize_function(examples):

       return tokenizer(examples['report'], padding="max_length", truncation=True)

   tokenized_dataset = df.apply(lambda x: tokenize_function(x), axis=1)

   ```

4. Convert Data to HuggingFace Dataset Format:

   ```python

   from datasets import Dataset

   dataset = Dataset.from_pandas(df)

   tokenized_dataset = dataset.map(tokenize_function, batched=True)

   ```

5. Save the Tokenized Dataset:

   ```python

   tokenized_dataset.save_to_disk("path_to_save_tokenized_dataset")

   ```

Example Pathology Lab Data Preparation Script

Here is a complete script to prepare pathology lab data for fine-tuning:

```python

import pandas as pd

from transformers import AutoTokenizer

from datasets import Dataset

# Load your dataset

df = pd.read_csv("pathology_lab_data.csv")

# Clean the dataset (remove rows with missing values)

df.dropna(inplace=True)

# Select relevant columns (e.g., 'report' and 'diagnosis')

df = df[['report', 'diagnosis']]

# Initialize the tokenizer

model_name = "pretrained_model_name"

tokenizer = AutoTokenizer.from_pretrained(model_name)

# Tokenize the data

def tokenize_function(examples):

    return tokenizer(examples['report'], padding="max_length", truncation=True)

dataset = Dataset.from_pandas(df)

tokenized_dataset = dataset.map(tokenize_function, batched=True)

# Save the tokenized dataset

tokenized_dataset.save_to_disk("path_to_save_tokenized_dataset")

```

Notes

- Handling Imbalanced Data: If your dataset is imbalanced (e.g., more reports for certain diagnoses), consider techniques like oversampling, undersampling, or weighted loss functions during fine-tuning.

- Data Augmentation: You may also use data augmentation techniques to artificially increase the size of your dataset.

By following these steps, you'll have a clean, tokenized dataset ready for fine-tuning a model on pathology lab data.

You can read my other article about data preparation.