Datasets:
File size: 4,935 Bytes
648963d afecc1c 648963d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 | ---
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) |