README.md

---
dataset_info:
  features:
  - name: title
    dtype: string
  - name: crawl_date
    dtype: string
  - name: url
    dtype: string
  - name: domain
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  - name: file_type
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  - name: languages
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  - name: document_fluency
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  - name: text
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  - name: paragraphs
    sequence:
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    - name: is_heading
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    - name: quality_label
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    - name: fluency
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    - name: language
      dtype: string
    - name: contains_sensitive
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  - name: sentence_count
    dtype: int64
  - name: paragraph_count
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  - name: character_length
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  - name: word_count
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  - name: phi_tokens
    sequence: int64
  - name: phi_token_count
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  - name: gemma2_tokens
    sequence: int64
  - name: gemma2_token_count
    dtype: int64
  - name: micka_tokens
    sequence: int64
  - name: micka_token_count
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  - name: orca_tokens
    sequence: int64
  - name: orca_token_count
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  - name: llama_tokens
    sequence: int64
  - name: llama_token_count
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  - name: micka_struct_tokens
    sequence: int64
  - name: micka_struct_token_count
    dtype: int64
  splits:
  - name: train
    num_bytes: 212190702185
    num_examples: 6302486
  download_size: 54394662084
  dataset_size: 212190702185
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc-by-sa-4.0
---

# Dataset Card for MaCoCu-sl Multi-Tokenized

**Dataset Description:**

This dataset provides a pre-tokenized version of the Slovene web corpus MaCoCu. 
It includes the original text data and metadata from MaCoCu-sl, augmented with token IDs and token counts generated by several 
popular large language model tokenizers. The goal is to facilitate research and experimentation by providing ready-to-use tokenized data, 
saving computational resources during repeated setups.

Licensing and orginal data is taken from https://www.clarin.si/repository/xmlui/handle/11356/1795.
This is just a repackaging for easier use with Machine Learning Frameworks, for licensing & use terms, see the original dataset.

**Tokenization Details:**

The dataset contains tokenizations for the following models, applied to each document in the `train` split of `klokedm/MaCoCu-sl`:

1.  **`microsoft/phi-4`**: Standard tokenization.
2.  **`google/gemma-2-2b`**: Standard tokenization.
3.  **`klokedm/micka-32768`**:
    * Standard tokenization (`micka_tokens`, `micka_token_count`).
    * Structured tokenization (`micka_struct_tokens`, `micka_struct_token_count`): Sentences (identified using NLTK for Slovene) are wrapped in `⸢s⸥...⸢/s⸥` tags, and paragraphs are wrapped in `⸢p⸥...⸢/p⸥` tags before tokenization.
4.  **`microsoft/Orca-2-13b`**: Standard tokenization.
5.  **`meta-llama/Llama-3.3-70B-Instruct`**: Standard tokenization.

**Input Text Preparation:**

* For standard tokenizations (Phi, Gemma2, Orca, Llama, standard Micka): The input text was primarily derived from the `text` field of the source dataset. If the `text` field was empty, paragraphs from the `paragraphs` field were joined by newlines (`\n`).
* For structured Micka tokenization: The input text was derived from the `paragraphs` field. If unavailable, the `text` field was split by newlines to simulate paragraphs. Each paragraph's text was then sentence-split using `nltk.sent_tokenize(..., language='slovene')`, and the structural tags were added as described above.

All tokenizations were performed using `add_special_tokens=True`.

**Additional Statistics:**

The following statistics were computed based on the flattened text (primarily from the `text` field, joined by newlines if applicable):

* `sentence_count`: Number of sentences identified using `nltk.sent_tokenize(..., language='slovene')`.
* `paragraph_count`: Number of paragraphs (derived from the `paragraphs` field structure or non-empty lines in the `text` field).
* `character_length`: Total number of characters in the flattened text.
* `word_count`: Number of words (whitespace-separated) in the flattened text.

**Data Fields:**

The dataset contains the following fields:

* `title`: (string) Document title if found, else empty.
* `crawl_date`: (string) Date of the web crawl (YYYY-MM-DD).
* `url`: (string) Source URL of the document.
* `domain`: (string) Domain name from the URL.
* `file_type`: (string) Detected file type (e.g., 'html', 'pdf').
* `languages`: (string) Detected language(s). Primarily 'sl'.
* `document_fluency`: (float32) Fluency score for the document.
* `text`: (string) Plain text content of the document.
* `paragraphs`: (list of dicts) Structured paragraph information from the source dataset (features: `paragraph_text`, `is_heading`, `quality_label`, `fluency`, `language`, `contains_sensitive`).
* `sentence_count`: (int64) Number of sentences computed for statistics.
* `paragraph_count`: (int64) Number of paragraphs computed for statistics.
* `character_length`: (int64) Character length computed for statistics.
* `word_count`: (int64) Word count computed for statistics.
* `phi_tokens`: (list of int64) Token IDs generated by `microsoft/phi-4` tokenizer.
* `phi_token_count`: (int64) Number of tokens in `phi_tokens`.
* `gemma2_tokens`: (list of int64) Token IDs generated by `google/gemma-2-2b` tokenizer.
* `gemma2_token_count`: (int64) Number of tokens in `gemma2_tokens`.
* `micka_tokens`: (list of int64) Token IDs generated by `klokedm/micka-32768` (standard).
* `micka_token_count`: (int64) Number of tokens in `micka_tokens`.
* `orca_tokens`: (list of int64) Token IDs generated by `microsoft/Orca-2-13b` tokenizer.
* `orca_token_count`: (int64) Number of tokens in `orca_tokens`.
* `llama_tokens`: (list of int64) Token IDs generated by `meta-llama/Llama-3.3-70B-Instruct` tokenizer.
* `llama_token_count`: (int64) Number of tokens in `llama_tokens`.
* `micka_struct_tokens`: (list of int64) Token IDs generated by `klokedm/micka-32768` (structured).
* `micka_struct_token_count`: (int64) Number of tokens in `micka_struct_tokens`.

**Data Splits:**

The dataset contains only the `train` split, mirroring the structure of `klokedm/MaCoCu-sl`. It includes all examples from the original training split.

**Source Dataset:**

Please refer to the [CLARIN MaCoCu-sl dataset](https://www.clarin.si/repository/xmlui/handle/11356/1795) for detailed information about the original data collection, cleaning, and filtering processes.

**Dataset Usage:**

Load the dataset using the `datasets` library:

```python
from datasets import load_dataset

# Load the repository from HF
repo_id = "klokedm/MaCoCu-sl-tokenized"
ds = load_dataset(repo_id)

# Access an example and specific tokenization
example = ds['train'][0]

print(f"URL: {example['url']}")
print(f"--- Phi Tokens ({example['phi_token_count']}) ---")
print(example['phi_tokens'][:20]) # Print first 20 tokens

print(f"--- Structured Micka Tokens ({example['micka_struct_token_count']}) ---")
print(example['micka_struct_tokens'][:20]) # Print first 20 tokens

print(f"--- Statistics ---")
print(f"Sentences: {example['sentence_count']}, Paragraphs: {example['paragraph_count']}")
print(f"Chars: {example['character_length']}, Words: {example['word_count']}")