rsateam/sr-bs-hr-language-id

SR/BS/HR Language Identification Dataset

Fine-grained language identification dataset for Serbian, Bosnian, and Croatian

RSA Team • GitHub • Website

Overview

This dataset enables fine-grained language identification between Serbian, Bosnian, and Croatian — three closely related South Slavic languages that are often misclassified by general-purpose language identification tools.

Derived from rsateam/sr-bs-hr-clean-text, this dataset uses source-based labeling to provide ground-truth labels without circular dependencies on automatic language detection.

The Challenge

Existing language identification tools often struggle with sr/bs/hr:

Tool	Problem
FastText lid.176	Often treats them as one language
langid.py	Low precision between sr/bs/hr
CLD3	Inconsistent across text lengths

This Dataset Enables

• Training specialized sr/bs/hr classifiers
• Benchmarking and evaluating LID systems
• Preprocessing pipeline routing for multilingual NLP
• Research on closely related language varieties

Dataset Statistics

Metric	Value
Total examples	60,000
Dataset size	27.4 MB
Download size	16.5 MB
Labels	3 (sr, bs, hr)

Splits

Split	Examples	Size
train	41,996	19.1 MB
validation	8,997	4.1 MB
test	9,007	4.1 MB

Label Distribution

Each split is balanced across languages (~33% each):

Label	Language
sr	Serbian
bs	Bosnian
hr	Croatian

Length Distribution

Bucket	Description
short	<100 characters
medium	100-300 characters
long	>300 characters

Dataset Structure

Data Format

{
  "id": "550e8400-e29b-41d4-a716-446655440000",
  "text": "Kratak pasus teksta za identifikaciju...",
  "label": "bs",
  "source": "bs.wikipedia.org",
  "length": 156,
  "length_bucket": "medium",
  "source_doc_id": "original-document-uuid"
}

Fields

Field	Type	Description
id	string	Unique sample identifier
text	string	Text sample for classification
label	string	Language label (sr/bs/hr)
source	string	Source domain
length	int	Length in characters
length_bucket	string	Length category (short/medium/long)
source_doc_id	string	Reference to source document in clean-text corpus

Labeling Methodology

Source-Based Labeling

Labels are assigned based on the source of publication, not automatic detection:

Source	Label
sr.wikipedia.org	sr
bs.wikipedia.org	bs
hr.wikipedia.org	hr

Why Source-Based?

This approach is academically accepted and avoids common pitfalls:

Advantage	Explanation
Ground truth	Labels reflect author/publisher intent
No circular dependency	Not trained on LID output
Reproducible	Deterministic labeling process
Transparent	Source field enables verification

Usage

Loading the Dataset

from datasets import load_dataset

# Load full dataset
dataset = load_dataset("rsateam/sr-bs-hr-language-id")

# Load specific split
train = load_dataset("rsateam/sr-bs-hr-language-id", split="train")
test = load_dataset("rsateam/sr-bs-hr-language-id", split="test")

Training a Classifier

from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from transformers import TrainingArguments, Trainer

dataset = load_dataset("rsateam/sr-bs-hr-language-id")

# Create label mapping
label2id = {"sr": 0, "bs": 1, "hr": 2}
id2label = {v: k for k, v in label2id.items()}

# Load model and tokenizer
model_name = "xlm-roberta-base"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(
    model_name,
    num_labels=3,
    label2id=label2id,
    id2label=id2label
)

# Tokenize
def tokenize(examples):
    tokens = tokenizer(examples["text"], truncation=True, max_length=256)
    tokens["labels"] = [label2id[label] for label in examples["label"]]
    return tokens

tokenized = dataset.map(tokenize, batched=True)

# Train
trainer = Trainer(
    model=model,
    args=TrainingArguments(
        output_dir="./sr-bs-hr-lid",
        num_train_epochs=3,
        per_device_train_batch_size=32,
        evaluation_strategy="epoch"
    ),
    train_dataset=tokenized["train"],
    eval_dataset=tokenized["validation"]
)

trainer.train()

Evaluating Existing LID Systems

from datasets import load_dataset
import fasttext

# Load dataset and FastText model
dataset = load_dataset("rsateam/sr-bs-hr-language-id", split="test")
lid_model = fasttext.load_model("lid.176.bin")

# Evaluate
correct = 0
for example in dataset:
    pred = lid_model.predict(example["text"].replace("\n", " "))[0][0]
    pred_lang = pred.replace("__label__", "")
    
    # Map FastText predictions to our labels
    if pred_lang in ["sr", "bs", "hr"]:
        if pred_lang == example["label"]:
            correct += 1

accuracy = correct / len(dataset)
print(f"FastText accuracy on sr/bs/hr: {accuracy:.2%}")

Filtering by Length

# Get only short texts for challenging evaluation
short_texts = dataset["test"].filter(lambda x: x["length_bucket"] == "short")

# Get long texts for easier classification
long_texts = dataset["test"].filter(lambda x: x["length_bucket"] == "long")

Relationship to Source Dataset

This dataset is derived from rsateam/sr-bs-hr-clean-text:

sr-bs-hr-clean-text (641K docs)
        │
        ▼
   [Sampling & Segmentation]
        │
        ▼
sr-bs-hr-language-id (60K samples)

The source_doc_id field links each sample back to its source document.

Considerations

Use Cases

• Primary: Training and evaluating language identification models
• Secondary: Preprocessing pipelines, dialect research, cross-lingual studies

Limitations

• Source-based labels may not capture individual variation
• Wikipedia style may differ from conversational text
• Some lexical overlap between languages is inherent

Ethical Considerations

• Derived from Wikipedia (CC-BY-SA)
• No personally identifiable information
• Language labels reflect publication source, not speaker identity

License

CC-BY-SA-4.0 — consistent with source dataset.

Citation

@dataset{rsateam_language_id_2026,
  title={SR/BS/HR Language Identification Dataset},
  author={RSA Team},
  year={2026},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/rsateam/sr-bs-hr-language-id},
  note={Fine-grained language identification for Serbian, Bosnian, and Croatian}
}

Related Datasets

Dataset	Description
rsateam/sr-bs-hr-clean-text	Source corpus (641K documents)

Contributing

For suggestions, bug reports, or improvements:

• Open an issue on GitHub
• Email: office@rsateam.com

---

RSA Team — Building bridges between languages and AI, one dataset at a time.