| --- |
| license: mit |
| task_categories: |
| - text-classification |
| - text-generation |
| language: |
| - en |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
|
|
| # UKS Seed Dataset Generator |
|
|
| Generates preprocessed training data for the UKS Energy System from the FineWeb dataset. |
|
|
| ## Overview |
|
|
| This pipeline processes text from HuggingFace's FineWeb dataset and produces two output formats: |
|
|
| 1. **pre_tokenized.jsonl.gz**: Ready for direct model training |
| 2. **intermediate.parquet**: For exploration and custom tokenization workflows |
| |
| ## Dependencies |
| |
| ```bash |
| pip install spacy transformers datasets tqdm |
| python -m spacy download en_core_web_sm |
| pip install pyarrow # for parquet output |
| ``` |
| |
| ## Tokenizer |
| |
| ### XLNetTokenizer |
| |
| Preprocessed data uses **[XLNetTokenizer](https://huggingface.co/xlnet/xlnet-base-cased)** for subword tokenization. |
|
|
| **Model**: `xlnet/xlnet-base-cased` |
| **Vocab size**: 32,000 tokens |
| **Features**: |
| - SentencePiece subword tokenization |
| - Punctuation-aware splitting |
| - Multilingual support built-in |
|
|
| **Example tokenization**: |
| ```python |
| from transformers import XLNetTokenizer |
| |
| tokenizer = XLNetTokenizer.from_pretrained("xlnet/xlnet-base-cased") |
| text = "Don't forget the meeting at 5pm." |
| |
| ids = tokenizer.encode(text, add_special_tokens=False) |
| tokens = tokenizer.convert_ids_to_tokens(ids) |
| # tokens: ["Don", "'", "t", "forget", "the", "meeting", "at", "5", "pm", "."] |
| # ids: [13645, 9, 24, 12087, 5, 4857, 19, 821, 131, 10] |
| ``` |
|
|
| ### spaCy Features |
|
|
| Linguistic annotations are extracted using **spaCy** (`en_core_web_sm`): |
|
|
| | Feature | Description | |
| |---------|-------------| |
| | `pos` | Part-of-speech tags (NOUN, VERB, ADJ, etc.) | |
| | `dep` | Dependency relations (nsubj, dobj, prep, etc.) | |
| | `lemma` | Lemmatized forms | |
| | `ent_type` | Named entity types (PERSON, ORG, DATE, etc.) | |
| | `head_dist` | Distance to head token in dependency tree | |
|
|
| ## Output Schema |
|
|
| Both output files contain the following columns: |
|
|
| | Column | Type | Description | Example | |
| |--------|------|-------------|---------| |
| | `id` | string | SHA256 hash (first 16 chars) | `"a1b2c3d4e5f6g7h8"` | |
| | `text` | string | Original sentence | `"The cat sat on the mat."` | |
| | `tokens` | string (JSON list) | XLNet token strings | `["The", "cat", "sat", ...]` | |
| | `token_ids` | string (JSON list) | XLNet integer IDs | `[44, 245, 1234, ...]` | |
| | `pos` | string (JSON list) | spaCy POS tags | `["DET", "NOUN", "VERB", ...]` | |
| | `dep` | string (JSON list) | Dependency labels | `["det", "nsubj", "ROOT", ...]` | |
| | `lemma` | string (JSON list) | Lemmas | `["the", "cat", "sit", ...]` | |
| | `ent_type` | string (JSON list) | Entity types | `["O", "O", "O", "DATE"]` | |
| | `head_dist` | string (JSON list) | Head distances | `[1, -2, 0, ...]` | |
|
|
| ### JSON List Format |
|
|
| All list columns are stored as JSON strings for compatibility: |
|
|
| ```python |
| import json |
| |
| # Loading tokens from file |
| record = json.loads(line) |
| tokens = json.loads(record["tokens"]) # ["The", "cat", "sat", ...] |
| ``` |
|
|
| ## Output Files |
|
|
| ### pre_tokenized.jsonl.gz |
| |
| Gzipped JSON Lines format for direct training consumption: |
| |
| ```python |
| import gzip |
| import json |
| |
| with gzip.open("output/pre_tokenized.jsonl.gz", "rt") as f: |
| for line in f: |
| record = json.loads(line) |
| # Use directly in training |
| ``` |
| |
| ### intermediate.parquet |
|
|
| Apache Parquet for data exploration: |
|
|
| ```python |
| import pyarrow.parquet as pq |
| |
| table = pq.read_table("output/intermediate.parquet") |
| df = table.to_pandas() |
| # Explore with pandas |
| ``` |
|
|
| ## Usage |
|
|
| ### Basic Generation |
|
|
| ```bash |
| cd hf_dataset |
| python generate.py |
| ``` |
|
|
| ### Custom Options |
|
|
| ```bash |
| python generate.py \ |
| --max-samples 50000 \ |
| --batch-size 200 \ |
| --output-dir ./my_output \ |
| --cache-dir ./my_cache |
| ``` |
|
|
| ### Python API |
|
|
| ```python |
| from generate import SEEDDatasetGenerator, SEEDConfig |
| |
| config = SEEDConfig( |
| max_samples=10000, |
| batch_size=100, |
| output_dir=Path("output"), |
| ) |
| |
| generator = SEEDDatasetGenerator(config) |
| paths = generator.generate() |
| |
| print(f"Generated: {paths}") |
| ``` |
|
|
| ## Configuration |
|
|
| | Parameter | Default | Description | |
| |-----------|---------|-------------| |
| | `max_samples` | 10,000 | Maximum sentences to process | |
| | `batch_size` | 100 | Documents per spaCy batch | |
| | `min_sentence_len` | 5 | Minimum sentence characters | |
| | `max_sentence_len` | 512 | Maximum sentence characters | |
| | `output_dir` | `hf_dataset/output` | Output directory | |
| | `cache_dir` | `hf_dataset/.cache` | Cache directory | |
| | `hf_dataset_name` | `HuggingFaceFW/fineweb` | Source dataset | |
| | `spacy_model` | `en_core_web_sm` | spaCy model name | |
| | `xlnet_model` | `xlnet/xlnet-base-cased` | HuggingFace model path | |
|
|
| ## Cache |
|
|
| The generator tracks processed samples in `cache_dir/processed_ids.json` to avoid duplicates and enable resuming interrupted runs. |
|
|
| ## Notes |
|
|
| - Sentences shorter than 5 characters are skipped |
| - Sentences longer than 512 characters are truncated |
| - Duplicates are detected via SHA256 hash |
| - Processsing is streamed from HuggingFace (memory efficient) |
