11.7.1 Project Roadmap: Build an Evaluatable NLP Pipeline

An NLP project is not a fluent paragraph. It is a clear task boundary, data source, baseline, evaluation method, failure analysis, and structured deliverable.

See the Project Evidence Loop First

Start with information extraction or classification for clear labels. Move to summarization and QA when you can evaluate factuality, refusal, citations, and boundaries.

Run a Project Readiness Check

project = {
    "task": "information extraction",
    "has_schema": True,
    "has_baseline": True,
    "has_eval_cases": True,
    "has_failure_case": True,
}

ready = all(project[key] for key in ["has_schema", "has_baseline", "has_eval_cases", "has_failure_case"])

print("task:", project["task"])
print("portfolio_ready:", ready)

Expected output:

task: information extraction
portfolio_ready: True

If labels, fields, or knowledge boundaries are unclear, fix the task definition before changing models.

Learn in This Order

Step	Project	Evidence
1	Information extraction	Schema, field boundaries, precision/recall, failure examples
2	Text classification	Labels, baseline, F1, ambiguity cases
3	Summarization	Compression, factuality, readability, missing facts
4	QA	Retrieval, citation, refusal, no-answer evaluation
5	Hands-on workshop	Reproducible mini pipeline before larger project pages

Run 11.7.6 Hands-on: Build a Reproducible NLP Mini Pipeline before expanding the project.

Project Deliverable Standards

Deliverable	Minimum Requirement	Stronger Portfolio Version
README	Goal, run command, dependencies, examples	Add task boundary, data source, trade-offs, review summary
Label/schema	Labels, entity boundaries, or output fields	Add positive, negative, boundary examples, consistency notes
Baseline	Keyword, TF-IDF, rule, or simple model	Add model comparison and error attribution
Evaluation	Accuracy, recall, F1, human score, or factuality check	Add analysis by label, length, domain, and noise type
Failure case	At least 1 real failure	Add cause, fix action, regression check
Presentation	Screenshot or short GIF proving it runs	Build a clear text-understanding project page

Evidence to Keep

Keep this page’s proof of learning as a small evidence card:

Task Output

label, entity fields, summary, answer, retrieval result, or semantic graph

Artifacts

raw text, processed text, predictions, metrics, and failure cases

Metric

accuracy/F1, precision/recall, retrieval hit rate, faithfulness, or schema validity

Failure Check

unclear labels, over-cleaning, boundary errors, hallucination, or unsupported answer

Expected Output

reproducible text pipeline folder with metrics and examples

Pass Check

You pass this chapter when your NLP project has a task definition, data examples, evaluation metric, baseline, failure case, and next-step improvement plan.

Check reasoning and explanation

1. A passing answer starts from the text unit and output type: token, span, sentence label, sequence, embedding, or generated text.
2. The evidence should include a small dataset example, model or pipeline choice, metric, and at least one inspected error case.
3. A good self-check distinguishes preprocessing issues from model issues, such as tokenization mistakes, label ambiguity, data imbalance, or hallucinated generation.