NLP Toolkit Guide

NLP Toolkit Guide

Overview

Natural Language Processing research requires a diverse set of analytical tools beyond standard model training. Text quality assessment, AI-generated text detection, linguistic feature extraction, and corpus analysis all depend on well-understood metrics: perplexity, burstiness, entropy, and their variants.

This guide provides practical implementations of these core NLP metrics alongside patterns for tokenization, embedding analysis, and text feature engineering. The focus is on metrics used in active research areas -- AI text detection (perplexity + burstiness classifiers), information-theoretic analysis of corpora, and linguistic diversity measurement.

These tools are framework-agnostic where possible, but leverage Hugging Face Transformers for language model operations and standard Python scientific computing libraries for statistical analysis.

Perplexity Scoring

Perplexity measures how well a language model predicts a text. Lower perplexity means the text is more predictable to the model -- a key signal in AI text detection, model evaluation, and domain adaptation.

import torch
import numpy as np
from transformers import AutoModelForCausalLM, AutoTokenizer

def compute_perplexity(text: str, model_name: str = "gpt2") -> dict:
    """
    Compute token-level and text-level perplexity using a causal LM.

    Returns:
        dict with 'perplexity', 'log_likelihood', 'token_perplexities'
    """
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForCausalLM.from_pretrained(model_name)
    model.eval()

    encodings = tokenizer(text, return_tensors="pt", truncation=True, max_length=1024)
    input_ids = encodings.input_ids

    with torch.no_grad():
        outputs = model(input_ids, labels=input_ids)
        neg_log_likelihood = outputs.loss.item()

    # Token-level perplexities for analysis
    with torch.no_grad():
        logits = outputs.logits[:, :-1, :]  # Shift for next-token prediction