---
language:
- tr
license: mit
tags:
- turkish
- roberta
- nlp
- masked-language-modeling
- turkish-nlp
thumbnail: latensis_logo.png
---

# Latensis — Turkish RoBERTa Base

<p align="center">
  <img src="latensis_logo.png" width="300"/>
</p>

> *Understanding Beyond the Visible*

**Latensis** is a Turkish-specific RoBERTa base model trained from scratch on 1GB 
of curated Turkish text using the [Hecemen Unigram 128k tokenizer](https://huggingface.co/mursideaki/hecemen-tokenizer-unigram-128k) — 
a tokenizer designed specifically for Turkish morphology.

## Why "Latensis"?

The name is derived from the Latin root *latent* (hidden, concealed) — a direct 
reference to the latent representations that embedding models learn. The suffix 
*-ensis* (of, belonging to) completes the name: **Latensis** roughly means 
*"of the latent space"* or *"that which belongs to what is hidden."*

## Model Details

| Property | Value |
|----------|-------|
| Architecture | RoBERTa base |
| Parameters | 184.5M |
| Vocabulary size | 128,000 |
| Tokenizer | Hecemen Unigram 128k |
| Training data | ~1GB Turkish text |
| Training steps | 500,000 |
| Val loss | 3.21 |
| Perplexity | 25 |

## Training Data

The model was trained on a carefully curated ~1GB Turkish corpus covering:
- News articles
- Books and literature
- Web content
- Legal and administrative texts
- Agricultural and domain-specific texts

## Benchmark Results

All fine-tuning experiments were conducted starting from this base model.
Comparisons are against BERTurk (`dbmdz/bert-base-turkish-cased`).

### Sentiment Analysis

| Dataset | Our Model | BERTurk | Training Data |
|---------|-----------|---------|---------------|
| TRSAv1 (Accuracy) | **0.9027** | 0.8372 | 19k examples |
| TRSAv1 (F1-macro) | **0.9026** | 0.8350 | 19k examples |
| Winvoker (Accuracy) | **0.8534** | 0.6944 | 19k examples |

### Natural Language Inference → RTE

| Dataset | Our Model | BERTurk | Training Data |
|---------|-----------|---------|---------------|
| RTE (Accuracy) | **0.7960** | 0.7780 | 50k NLI examples |
| RTE (F1-macro) | **0.7959** | 0.7770 | 50k NLI examples |

> BERTurk NLI model was trained on 482k NLI examples. Our model achieves 
> better results with only 50k examples (~10x less data).

### Named Entity Recognition

| Dataset | Our Model | BERTurk | Training Data |
|---------|-----------|---------|---------------|
| WikiANN-TR (F1-macro) | 0.9429 | 0.9522 | 20k examples |

> Within BERTurk's reported variance range (93.92 ± 0.07).

### Semantic Textual Similarity

| Metric | Our Model | Emrecan | Training Data |
|--------|-----------|---------|---------------|
| Pearson | 0.7687 | 0.8340 | 50k NLI + TrGLUE STS |
| Spearman | 0.7976 | 0.8300 | 50k NLI + TrGLUE STS |

> Emrecan model uses 482k NLI + full STS-b-TR. Our model uses 50k NLI (~10x less).

### QNLI

| Dataset | Our Model | Training Data |
|---------|-----------|---------------|
| TrGLUE QNLI (Accuracy) | 0.8749 | 120k examples |

## Key Findings
✅ Outperforms BERTurk on Sentiment (TRSAv1 +6pp, Winvoker +16pp)
✅ Outperforms BERTurk on RTE with 10x less NLI training data
✅ Competitive NER performance within BERTurk's variance range
✅ Competitive STS with 10x less training data than Emrecan
✅ All results achieved with only 1GB training data

## Usage

### As a base model for fine-tuning

```python
from transformers import RobertaModel, AutoTokenizer
import sentencepiece as spm
from huggingface_hub import hf_hub_download

# Load tokenizer
model_path = hf_hub_download(
    repo_id="mursideaki/hecemen-tokenizer-unigram-128k",
    filename="tr_unigram_tokenizer.model"
)
sp = spm.SentencePieceProcessor()
sp.load(model_path)

# Load model
model = RobertaModel.from_pretrained("mursideaki/latensis-roberta-base-tr")
```

### Masked Language Modeling

```python
from transformers import pipeline

mlm = pipeline(
    "fill-mask",
    model="mursideaki/latensis-roberta-base-tr"
)
# Note: Use with Hecemen tokenizer for best results
```

## Companion Models & Tokenizers

- [hecemen-tokenizer-unigram-128k](https://huggingface.co/mursideaki/hecemen-tokenizer-unigram-128k) — Tokenizer used for training
- [hecemen-tokenizer-bpe-96k](https://huggingface.co/mursideaki/hecemen-tokenizer-bpe-96k) — BPE variant
- Fine-tuned sentiment model *(coming soon)*

## Citation

```bibtex
@misc{latensis2026,
  author    = {Mürşide Aki},
  title     = {Latensis: A Turkish RoBERTa Base Model},
  year      = {2026},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/mursideaki/latensis-roberta-base-tr}
}
```

## License

MIT