latensis-roberta-base-tr

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 — 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

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

Natural Language Inference → RTE

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

Named Entity Recognition

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

Semantic Textual Similarity

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

QNLI

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

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

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 — Tokenizer used for training
• hecemen-tokenizer-bpe-96k — BPE variant
• Fine-tuned sentiment model (coming soon)

Citation

@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