--- dataset_info: features: - name: title dtype: string - name: crawl_date dtype: string - name: url dtype: string - name: domain dtype: string - name: file_type dtype: string - name: languages dtype: string - name: document_fluency dtype: float32 - name: text dtype: string - name: paragraphs sequence: - name: paragraph_text dtype: string - name: is_heading dtype: bool - name: quality_label dtype: string - name: fluency dtype: float32 - name: language dtype: string - name: contains_sensitive dtype: bool - name: sentence_count dtype: int64 - name: paragraph_count dtype: int64 - name: character_length dtype: int64 - name: word_count dtype: int64 - name: phi_tokens sequence: int64 - name: phi_token_count dtype: int64 - name: gemma2_tokens sequence: int64 - name: gemma2_token_count dtype: int64 - name: micka_tokens sequence: int64 - name: micka_token_count dtype: int64 - name: orca_tokens sequence: int64 - name: orca_token_count dtype: int64 - name: llama_tokens sequence: int64 - name: llama_token_count dtype: int64 - 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']}")