Objective

Tokenize the cleaned employees_cleaned.csv dataset to prepare the text data for embedding generation. Tokenization splits text into smaller units (tokens) that can be processed effectively by embedding models.

Dataset

• File Name: employees_cleaned.csv

• Columns to Tokenize:

  • name: Employee names.

  • role: Job roles.

  • department: Department names.

Steps to Tokenize Employee Profiles

1. Load the Cleaned Dataset

• Task: Load the employees_cleaned.csv file using the pandas library.

Sample Code:

python

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import pandas as pd

# Load the cleaned dataset

file_path = "employees_cleaned.csv"

df = pd.read_csv(file_path)

# Display the first few rows

print(df.head())

2. Install Tokenization Libraries

Install libraries if not already installed:
bash
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pip install nltk spacy

3. Tokenize the Text Columns

Option 1: Using NLTK

• Use nltk for word-level tokenization.

Sample Code:

python

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import nltk

from nltk.tokenize import word_tokenize

# Download necessary NLTK data

nltk.download('punkt')

# Tokenize text columns

df['name_tokens'] = df['name'].apply(word_tokenize)

df['role_tokens'] = df['role'].apply(word_tokenize)

df['department_tokens'] = df['department'].apply(word_tokenize)

# Display the tokenized dataset

print(df[['name_tokens', 'role_tokens', 'department_tokens']].head())

Option 2: Using spaCy

• Use spaCy for advanced tokenization that handles linguistic nuances.

Setup spaCy:

bash

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python -m spacy download en_core_web_sm

Sample Code:

python

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import spacy

# Load the spaCy language model

nlp = spacy.load('en_core_web_sm')

# Tokenization function

def spacy_tokenize(text):

    return [token.text for token in nlp(text)]

# Apply tokenization to columns

df['name_tokens'] = df['name'].apply(spacy_tokenize)

df['role_tokens'] = df['role'].apply(spacy_tokenize)

df['department_tokens'] = df['department'].apply(spacy_tokenize)

# Display the tokenized dataset

print(df[['name_tokens', 'role_tokens', 'department_tokens']].head())

4. Save the Tokenized Data

• Save the tokenized dataset for embedding generation.

Sample Code:

python

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# Save tokenized dataset

output_file_path = "employees_tokenized.csv"

df.to_csv(output_file_path, index=False)

print(f"Tokenized data saved to {output_file_path}")

Expected Output

After completing the tokenization:

1. New columns (name_tokens, role_tokens, department_tokens) contain lists of tokens for each row.

2. The tokenized dataset is saved as employees_tokenized.csv.

Sample Output:

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name_tokens         role_tokens           department_tokens

['john', 'doe']     ['software', 'engineer']  ['development']

['jane', 'smith']   ['data', 'scientist']     ['analytics']

['alice', 'brown']  ['project', 'manager']    ['management']

Extensions

1. Advanced Tokenization:

   • Explore subword tokenization (e.g., Byte Pair Encoding or WordPiece) for embedding models like BERT.

   • Libraries: HuggingFace tokenizers.

2. Validation:

   • Verify token counts for each column.

   • Ensure special characters are handled appropriately.

Validation Code:

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# Check token counts

df['name_token_count'] = df['name_tokens'].apply(len)

print(df[['name_token_count']].describe())

Learning Outcomes

1. Gain practical experience in tokenizing text data.

2. Learn to use tokenization libraries like NLTK and spaCy.

3. Prepare tokenized text for embedding generation and downstream NLP tasks.

Objective

Implement an advanced tokenization technique (e.g., using spaCy or HuggingFace Tokenizers) and compare its results with basic word tokenization (e.g., using nltk). This assignment will help evaluate the effectiveness of advanced methods in handling edge cases like subwords, punctuation, and special tokens.

Steps to Complete the Assignment

1. Install Required Libraries

Install the necessary libraries for advanced tokenization:
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pip install nltk spacy transformers

Download models or tokenization data:
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python -m spacy download en_core_web_sm

2. Prepare the Dataset

Load the cleaned employee profiles dataset (employees_cleaned.csv).

Sample Code:

python

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import pandas as pd

# Load the cleaned dataset

file_path = "employees_cleaned.csv"

df = pd.read_csv(file_path)

# Display the first few rows

print(df.head())

3. Basic Word Tokenization

Use nltk for basic word-level tokenization as the baseline.

Sample Code:

python

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import nltk

from nltk.tokenize import word_tokenize

# Download necessary NLTK data

nltk.download('punkt')

# Apply basic word tokenization

df['name_tokens_nltk'] = df['name'].apply(word_tokenize)

df['role_tokens_nltk'] = df['role'].apply(word_tokenize)

df['department_tokens_nltk'] = df['department'].apply(word_tokenize)

# Display the tokenized columns

print(df[['name_tokens_nltk', 'role_tokens_nltk', 'department_tokens_nltk']].head())

4. Advanced Tokenization with spaCy

Use spaCy for tokenization, which incorporates linguistic nuances.

Sample Code:

python

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import spacy

# Load spaCy language model

nlp = spacy.load('en_core_web_sm')

# Tokenization function

def spacy_tokenize(text):

    return [token.text for token in nlp(text)]

# Apply spaCy tokenization

df['name_tokens_spacy'] = df['name'].apply(spacy_tokenize)

df['role_tokens_spacy'] = df['role'].apply(spacy_tokenize)

df['department_tokens_spacy'] = df['department'].apply(spacy_tokenize)

# Display the tokenized columns

print(df[['name_tokens_spacy', 'role_tokens_spacy', 'department_tokens_spacy']].head())

5. Advanced Tokenization with HuggingFace Tokenizers

Use HuggingFace’s tokenizers library for subword-level tokenization (e.g., WordPiece or Byte Pair Encoding).

Sample Code:

python

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from transformers import AutoTokenizer

# Load HuggingFace tokenizer (e.g., BERT tokenizer)

tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")

# Tokenization function

def huggingface_tokenize(text):

    tokens = tokenizer.tokenize(text)

    return tokens

# Apply HuggingFace tokenization

df['name_tokens_hf'] = df['name'].apply(huggingface_tokenize)

df['role_tokens_hf'] = df['role'].apply(huggingface_tokenize)

df['department_tokens_hf'] = df['department'].apply(huggingface_tokenize)

# Display the tokenized columns

print(df[['name_tokens_hf', 'role_tokens_hf', 'department_tokens_hf']].head())

6. Save the Tokenized Dataset

Save the results for analysis and comparison.

Sample Code:

python

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output_file_path = "employees_tokenized_advanced.csv"

df.to_csv(output_file_path, index=False)

print(f"Tokenized data saved to {output_file_path}")

7. Compare Results

Evaluate the differences in tokenization approaches:

• Length of Tokens: Compare the number of tokens generated.

• Handling Special Cases: Evaluate punctuation, contractions, and subwords.

Comparison Code:

python

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# Compare token counts

df['name_token_count_nltk'] = df['name_tokens_nltk'].apply(len)

df['name_token_count_spacy'] = df['name_tokens_spacy'].apply(len)

df['name_token_count_hf'] = df['name_tokens_hf'].apply(len)

# Display comparison

comparison = df[['name', 'name_token_count_nltk', 'name_token_count_spacy', 'name_token_count_hf']]

print(comparison.head())

8. Write a Brief Report

Summarize the findings of your comparison:

• Accuracy: Which method captures linguistic nuances better (e.g., handling punctuation, contractions)?

• Token Count: Are there significant differences in the number of tokens generated?

• Suitability for Embeddings: Which method is more suitable for embedding generation?

Example Report Template:

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### Tokenization Comparison Report

#### Dataset: employees_cleaned.csv

#### Methods Compared:

1. **nltk**: Basic word-level tokenization.

2. **spaCy**: Linguistically informed tokenization.

3. **HuggingFace**: Subword-level tokenization (BERT tokenizer).

#### Key Findings:

1. **Token Count**:

   - nltk: Average tokens per name: X

   - spaCy: Average tokens per name: Y

   - HuggingFace: Average tokens per name: Z

2. **Handling Edge Cases**:

   - nltk struggled with contractions (e.g., "can't" → ["can", "'t"]).

   - spaCy handled contractions better (e.g., "can't" → ["ca", "n't"]).

   - HuggingFace split subwords effectively for embeddings (e.g., "can't" → ["can", "##'t"]).

3. **Suitability for Embeddings**:

   - HuggingFace tokenization is most suitable due to subword handling, which aligns with modern embedding models.

#### Conclusion:

For embedding generation, HuggingFace tokenization provides the most comprehensive results, followed by spaCy for linguistically informed tasks. nltk is sufficient for simple tasks but lacks advanced capabilities.

Deliverables

1. Python Script:

   • Includes both basic and advanced tokenization implementations.

   • Saves the tokenized dataset as employees_tokenized_advanced.csv.

2. Comparison Report:

   • Summary of findings and recommendations based on the analysis.

Learning Outcomes

1. Understand the advantages of advanced tokenization techniques.

2. Learn to use spaCy and HuggingFace for NLP tasks.

3. Develop a critical approach to evaluating preprocessing methods for embeddings.