suur-data 1.0.1

Intelligent data ingestion and tokenization pipeline

Suur Data

Intelligent data ingestion and tokenization pipeline.

Suur Data fetches text from any source, filters it by topic using a neural relevance scorer, then tokenizes it using either a pretrained HuggingFace tokenizer or a custom-trained BPE tokenizer.

---

Installation

# Core
pip install suur-data

# With all optional formats + HuggingFace tokenizers
pip install suur-data[all]

---

Python API

from suur_data import suur_data

# Minimal — fetches URL, no filter, GPT-2 tokenizer
tokens = suur_data("https://en.wikipedia.org/wiki/Neuroscience")

# Filter by topic, custom BPE tokenizer
tokens = suur_data(
    "research_paper.pdf",
    topic="quantum computing",
    tokenizer="custom",
    vocab_size=4000,
    save_dir="./my_tokenizer",
)

# Local file, pretrained BERT tokenizer, strict filter
tokens = suur_data(
    "~/corpus/biology.txt",
    topic="cell biology",
    tokenizer="pretrained",
    model="bert",
    threshold=0.10,
)

# Skip the filter entirely
tokens = suur_data("data.csv", no_filter=True)

print(tokens[:20])   # list of integer token IDs
print(len(tokens))   # total token count

Parameters

Parameter	Type	Default	Description
data_location	str	—	URL or local file path
topic	str	""	Subject for relevance filtering (empty = skip filter)
tokenizer	str	"pretrained"	"pretrained" or "custom"
model	str	"gpt2"	HuggingFace model shortcut or full ID
vocab_size	int	8000	BPE vocab size for custom tokenizer
threshold	float	`0.05``	Cosine similarity cutoff (0.0–1.0)
save_dir	str	None	Path to save tokenizer files
no_filter	bool	False	Skip the relevance filter
verbose	bool	True	Show progress output

Returns

List[int] — flat list of integer token IDs.

---

CLI

# Basic URL fetch
suur_data fetch https://example.com/article --topic "machine learning"

# PDF with custom BPE tokenizer
suur_data fetch paper.pdf --topic "protein folding" --tokenizer custom --vocab-size 6000

# Local file, pretrained BERT, save tokenizer
suur_data fetch corpus.txt --tokenizer pretrained --model bert --save-dir ./bert_tok

# Skip filter, save tokens to file
suur_data fetch data.json --no-filter --output tokens.json

# See supported models
suur_data models

# See supported file formats
suur_data formats

---

Supported Input Formats

Format	Notes
.txt, .md, .rst	Plain text
.pdf	Requires pdfminer.six
.docx	Requires python-docx
.csv, .tsv	All cells joined as text
.json	Recursively flattened key-value pairs
.html, .htm	Scripts/styles stripped (requires beautifulsoup4)
.epub	E-books (requires ebooklib + beautifulsoup4)
HTTP/HTTPS URL	Auto-downloaded, then parsed by extension

---

Pretrained Model Shortcuts

Shortcut	Model
gpt2	GPT-2 (OpenAI)
bert	BERT base uncased
roberta	RoBERTa base
distilbert	DistilBERT base uncased
t5	T5 small

You can also pass any HuggingFace Hub model ID directly:

--model "facebook/opt-125m"

---

Architecture

Source (URL / file)
        │
        ▼
  Stage 1: Ingest
  Handles 8 file types + HTTP download
        │
        ▼
  Stage 2: Neural Filter
  Splits into paragraph chunks
  Scores each chunk against topic via TF-IDF cosine similarity
  Drops chunks below threshold
        │
        ▼
  Stage 3: Tokenize
  ┌─────────────────────┐  ┌────────────────────────────┐
  │  Pretrained mode    │  │  Custom mode               │
  │  HuggingFace        │  │  BPE trainer (HF library   │
  │  AutoTokenizer      │  │  or pure-Python fallback)  │
  └─────────────────────┘  └────────────────────────────┘
        │
        ▼
  List[int]  ←  token IDs

---

Dependency Matrix

Feature	Required packages
Core pipeline	requests, beautifulsoup4, scikit-learn, numpy, click, chardet
PDF support	pdfminer.six
.docx support	python-docx
.epub support	ebooklib
Pretrained tokenizers	transformers
Fast BPE training	tokenizers

All optional — the tool degrades gracefully with built-in fallbacks when optional packages are missing.