---
license: mit
language:
- sk
datasets:
- oscar-corpus/OSCAR-2109
pipeline_tag: fill-mask
library_name: transformers
tags:
- slovak-language-model
---
# Slovak Morphological Baby Language Model (SK_Morph_BLM)

**SK_Morph_BLM** is a pretrained small language model for the Slovak language, based on the RoBERTa architecture. The model utilizes a custom morphological tokenizer (**SKMT**, more info [here](https://github.com/daviddrzik/Slovak_subword_tokenizers)) specifically designed for the Slovak language, which focuses on **preserving the integrity of root morphemes**. This tokenizer is not compatible with the standard `RobertaTokenizer` from the Hugging Face library due to its unique approach to tokenization. The model is case-insensitive, meaning it operates in lowercase. While the pretrained model can be used for masked language modeling, it is primarily intended for fine-tuning on downstream NLP tasks.

## How to Use the Model

To use the SK_Morph_BLM model, follow these steps:

```python
import torch
import sys
from transformers import AutoModelForMaskedLM
from huggingface_hub import snapshot_download

# Download the repository from Hugging Face and append the path to sys.path
repo_path = snapshot_download(repo_id="daviddrzik/SK_Morph_BLM")
sys.path.append(repo_path)

# Import the custom tokenizer from the downloaded repository
from SKMT_lib_v2.SKMT_BPE import SKMorfoTokenizer

# Initialize the tokenizer and model
tokenizer = SKMorfoTokenizer()
model = AutoModelForMaskedLM.from_pretrained("daviddrzik/SK_Morph_BLM")

# Function to fill in the masked token in a given text
def fill_mask(tokenized_text, tokenizer, model, top_k=5):
    inputs = tokenizer.tokenize(tokenized_text.lower(), max_length=256, return_tensors='pt', return_subword=False)
    mask_token_index = torch.where(inputs["input_ids"][0] == 4)[0]
    with torch.no_grad():
        predictions = model(**inputs)

    topk_tokens = torch.topk(predictions.logits[0, mask_token_index], k=top_k, dim=-1).indices

    fill_results = []
    for idx, i in enumerate(mask_token_index):
        for j, token_idx in enumerate(topk_tokens[idx]):
            token_text = tokenizer.convert_ids_to_tokens(token_idx.item())
            token_text = token_text.replace("Ġ", " ")  # Replace special characters with a space
            probability = torch.softmax(predictions.logits[0, i], dim=-1)[token_idx].item()
            fill_results.append({
                'score': probability,
                'token': token_idx.item(),
                'token_str': token_text,
                'sequence': tokenized_text.replace("<mask>", token_text.strip())
            })

    fill_results.sort(key=lambda x: x['score'], reverse=True)
    return fill_results

# Example usage of the function
text = "Včera večer sme <mask> nový film v kine, ktorý mal premiéru iba pred týždňom."
result = fill_mask(text.lower(), tokenizer, model, top_k=5)
print(result)

[{'score': 0.4014046788215637,
  'token': 6626,
  'token_str': ' videli',
  'sequence': 'včera večer sme videli nový film v kine, ktorý mal premiéru iba pred týždňom.'},
 {'score': 0.15018892288208008,
  'token': 874,
  'token_str': ' mali',
  'sequence': 'včera večer sme mali nový film v kine, ktorý mal premiéru iba pred týždňom.'},
 {'score': 0.057530131191015244,
  'token': 21193,
  'token_str': ' pozreli',
  'sequence': 'včera večer sme pozreli nový film v kine, ktorý mal premiéru iba pred týždňom.'},
 {'score': 0.049020398408174515,
  'token': 26468,
  'token_str': ' sledovali',
  'sequence': 'včera večer sme sledovali nový film v kine, ktorý mal premiéru iba pred týždňom.'},
 {'score': 0.04107135161757469,
  'token': 9171,
  'token_str': ' objavili',
  'sequence': 'včera večer sme objavili nový film v kine, ktorý mal premiéru iba pred týždňom.'}]
```

## Training Data

The `SK_Morph_BLM` model was pretrained using a subset of the OSCAR 2019 corpus, specifically focusing on the Slovak language. The corpus underwent comprehensive preprocessing to ensure the quality and relevance of the data:

- **Language Filtering:** Non-Slovak text was removed to focus solely on the Slovak language.
- **Character Normalization:** Various types of spaces, quotes, dashes, and separators were standardized (e.g., replacing different types of spaces with a single space, or dashes with hyphens). Emoticons were replaced with spaces.
- **Symbol and Unwanted Text Removal:** Sentences containing mathematical symbols, pictograms, or characters from Asian and African languages were deleted. Duplicates of punctuation, special characters, and spaces were also removed.
- **URL and Text Normalization:** All web addresses were removed, and the text was converted to lowercase to simplify tokenization.
- **Content Cleanup:** Text that included irrelevant content from web crawling, such as keywords and HTML tags, was identified and removed.

Additionally, the preprocessing included further refinement steps to create the final dataset:

- **Parentheses Content Removal:** All content within parentheses was removed to reduce noise.
- **Selection of Text Segments:** Medium-length text paragraphs were selected to maintain consistency.
- **Similarity Filtering:** Paragraphs with at least 50% similarity to previous ones were removed to minimize redundancy.
- **Random Sampling:** Finally, 20% of the remaining paragraphs were randomly selected.

After preprocessing, the training corpus consisted of:
- **455 MB of text**
- **895,125 paragraphs**
- **64.6 million words**
- **1.13 million unique words**
- **119 unique characters**

## Pretraining

The `SK_Morph_BLM` model was trained with the following key parameters:

- **Architecture:** Based on RoBERTa, with 6 hidden layers and 12 attention heads.
- **Hidden size:** 576
- **Vocabulary size:** 50,264 tokens
- **Sequence length:** 256 tokens
- **Dropout:** 0.1
- **Number of parameters:** 58 million
- **Optimizer:** AdamW, learning rate 1×10^(-4), weight decay 0.01
- **Training:** 30 epochs, divided into 3 phases:
  - **Phase 1:** 10 epochs on CPU (4x AMD EPYC 7542), batch size 64, 50 hours per epoch, 139,870 steps total.
  - **Phase 2:** 5 epochs on GPU (1x Nvidia A100 40GB), batch size 64, 100 minutes per epoch, 69,935 steps total.
  - **Phase 3:** 15 epochs on GPU (2x Nvidia A100 40GB), batch size 128, 60 minutes per epoch, 104,910 steps total.

The model was trained using the Hugging Face library, but without using the `Trainer` class—native PyTorch was used instead.

## Fine-Tuned Versions of the SK_Morph_BLM Model

Here are the fine-tuned versions of the `SK_Morph_BLM` model based on the folders provided:

- [`SK_Morph_BLM-ner`](https://huggingface.co/daviddrzik/SK_Morph_BLM-ner): Fine-tuned for Named Entity Recognition (NER) tasks.
- [`SK_Morph_BLM-pos`](https://huggingface.co/daviddrzik/SK_Morph_BLM-pos): Fine-tuned for Part-of-Speech (POS) tagging.
- [`SK_Morph_BLM-qa`](https://huggingface.co/daviddrzik/SK_Morph_BLM-qa): Fine-tuned for Question Answering tasks.
- [`SK_Morph_BLM-sentiment-csfd`](https://huggingface.co/daviddrzik/SK_Morph_BLM-sentiment-csfd): Fine-tuned for sentiment analysis on the CSFD (movie review) dataset.
- [`SK_Morph_BLM-sentiment-multidomain`](https://huggingface.co/daviddrzik/SK_Morph_BLM-sentiment-multidomain): Fine-tuned for sentiment analysis across multiple domains.
- [`SK_Morph_BLM-sentiment-reviews`](https://huggingface.co/daviddrzik/SK_Morph_BLM-sentiment-reviews): Fine-tuned for sentiment analysis on general review datasets.
- [`SK_Morph_BLM-topic-news`](https://huggingface.co/daviddrzik/SK_Morph_BLM-topic-news): Fine-tuned for topic classification in news articles.

## Citation

If you find our model or paper useful, please consider citing our work:

### Article:
Držík, D., & Forgac, F. (2024). Slovak morphological tokenizer using the Byte-Pair Encoding algorithm. PeerJ Computer Science, 10, e2465. https://doi.org/10.7717/peerj-cs.2465

### BibTeX Entry:
```bib
@article{drzik2024slovak,
  title={Slovak morphological tokenizer using the Byte-Pair Encoding algorithm},
  author={Držík, Dávid and Forgac, František},
  journal={PeerJ Computer Science},
  volume={10},
  pages={e2465},
  year={2024},
  month={11},
  issn={2376-5992},
  doi={10.7717/peerj-cs.2465}
}
```