Pylate Day 1

Trying to explore pylate and create my first Arabic model

PyLate & ColBERT Evaluation - Complete Learning Guide

Core Concepts

What is PyLate?

Q: What is PyLate and what does it do? A: PyLate is a Python library for vector retrieval and search, specifically designed for ColBERT models. It provides tools for indexing documents, encoding queries, and performing similarity search.

What is ColBERT?

Q: How does ColBERT work? A: ColBERT (Contextualized Late Interaction over BERT) creates dense vector representations for documents and queries, then uses late interaction (token-level matching) for retrieval instead of single vector similarity.

Dataset Formats

Format 1: Triplet/Multi-negative Format

Q: What does triplet format look like? A: Each row contains: - query: The search query - positive: One relevant document
- negative1, negative2, …: Multiple irrelevant documents

Pros: Good for training with hard negatives Cons: No separate corpus, harder to evaluate

Format 2: Structured Retrieval Format

Q: What does structured format contain? A: Three separate components: - corpus: All documents with IDs - queries: All queries with IDs - qrels: Relevance judgments (query-doc pairs)

Pros: Standard IR evaluation format, works with PyLate directly Cons: More complex structure

Format 3: Passage Ranking Format

Q: How does passage ranking format work? A: Each row has: - query_id, query: Query information - positive_passages: List of relevant documents - negative_passages: List of irrelevant documents

Pros: Multiple positives/negatives per query, rich annotations Cons: Requires extraction to create corpus

Data Conversion Strategies

Memory-Efficient File Writing

Q: How do you handle large datasets efficiently? A: Stream processing with direct file writing:

with open('corpus.jsonl', 'w') as f:
    for el in dataset['corpus']:
        if el['corpus-id'] and el['text']:
            json.dump({"_id": el['corpus-id'], "text": el['text']}, f)
            f.write('\n')

Pros: Low memory usage, handles any dataset size Cons: Requires file I/O, slightly slower

PyLate File Requirements

Q: What files does BEIR datasets is ? A: - corpus.jsonl: {"_id": "doc1", "text": "document text"} - queries.jsonl: {"_id": "q1", "text": "query text"} - qrels/split.tsv: query_id\tdoc_id\tscore

Critical: File names and folder structure must match exactly

Direct Dictionary Conversion

Q: How do you convert without files? A: Transform to PyLate’s expected return format:

documents = [{"id": doc_id, "text": text} for doc_id, text in corpus.items()]
queries = list(queries.values())
# qrels stays as dictionary

Pros: Faster, no file operations Cons: Must match exact format, harder to debug

Evaluation Approaches

Custom Evaluation with ranx

Q: When do you use ranx for evaluation? A: When you have non-standard formats or want custom metrics:

qrels = Qrels(qrels_dict)
run = Run(run_dict)
metrics = evaluate(qrels, run, ["ndcg@5", "map@5"])

Pros: Flexible, works with any format Cons: Manual setup required

PyLate Built-in Evaluation

Q: When do you use PyLate’s evaluation? A: When data is in standard BEIR format with proper file structure

Pros: Standardized, less code Cons: Strict format requirements

Indexing Strategies

PLAID Index

Q: What is PLAID indexing? A: PyLate’s efficient indexing method for ColBERT embeddings, supporting fast similarity search

Key Parameters: - index_folder: Where to store index - index_name: Identifier for the index
- override=True: Overwrites existing index

Document Processing

Q: How do you prepare documents for indexing? A: 1. Extract unique documents from all sources 2. Create document IDs and embeddings 3. Add to index with add_documents()

Important: Use is_query=False for documents, is_query=True for queries

Common Pitfalls & Solutions

File Format Issues

Q: What are common file format mistakes? A: - Wrong file extensions (.csv instead of .tsv) - Incorrect folder structure (missing qrels folder) - Wrong field names (id vs _id)

Data Extraction Problems

Q: How do you handle variable-length lists? A: Use nested loops for negative passages:

for row in dataset:
    for neg_doc in row['negative_passages']:
        # process each negative document

Memory Management

Q: How do you avoid memory issues? A: - Process datasets in chunks - Use generators instead of lists - Write to files incrementally - Use dictionaries to avoid duplicates

Performance Considerations

Batch Processing

Q: How do you optimize encoding speed? A: Use appropriate batch sizes: - Larger batches: Faster but more memory - Smaller batches: Slower but memory-safe - Typical: batch_size=32 or batch_size=64

Index Management

Q: How do you manage multiple indexes? A: Use descriptive names and separate folders: - index_folder="arabic_index" - index_name="gte-multilingual-base"

Evaluation Metrics

Standard IR Metrics

Q: What metrics should you track? A: - NDCG@k: Normalized discounted cumulative gain - MAP@k: Mean average precision
- Recall@k: Proportion of relevant docs retrieved - Precision@k: Proportion of retrieved docs that are relevant

Interpreting Results

Q: What makes good retrieval performance? A: - NDCG@5 > 0.7: Excellent - NDCG@5 > 0.5: Good
- NDCG@5 > 0.3: Acceptable - NDCG@5 < 0.3: Needs improvement

This comprehensive guide covers all the key concepts, trade-offs, and practical considerations for working with PyLate and ColBERT evaluation pipelines.