latensis-sentiment-tr

Latensis Sentiment — Turkish Sentiment Analysis

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

Understanding Beyond the Visible

Latensis Sentiment is a Turkish sentiment analysis model fine-tuned on top of Latensis RoBERTa Base — a Turkish-specific RoBERTa model trained from scratch with the Hecemen Unigram 128k tokenizer.

Labels

Benchmark Results

Evaluated on two independent test sets. Comparison against savasy/bert-base-turkish-sentiment-cased (BERTurk Sentiment).

On Winvoker test set, our model outperforms BERTurk by +16pp accuracy — notably, BERTurk was trained on Winvoker data while our model had never seen it.

Training Details

Usage

import torch
import sentencepiece as spm
from transformers import RobertaForSequenceClassification, RobertaConfig
from huggingface_hub import hf_hub_download

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

PAD_ID = sp.piece_to_id("<pad>")
BOS_ID = sp.piece_to_id("<s>")
EOS_ID = sp.piece_to_id("</s>")
MAX_LENGTH = 256

def tokenize(text):
    ids  = sp.encode_as_ids(str(text))
    ids  = [BOS_ID] + ids[:MAX_LENGTH-2] + [EOS_ID]
    mask = [1] * len(ids)
    if len(ids) < MAX_LENGTH:
        pad_len = MAX_LENGTH - len(ids)
        ids  = ids  + [PAD_ID] * pad_len
        mask = mask + [0]      * pad_len
    return ids, mask

# Load model
config = RobertaConfig.from_pretrained("mursideaki/latensis-sentiment-tr")
model  = RobertaForSequenceClassification.from_pretrained(
    "mursideaki/latensis-sentiment-tr",
    config=config
)
model.eval()

# Predict
def predict(text):
    ids, mask = tokenize(text)
    input_ids      = torch.tensor([ids],  dtype=torch.long)
    attention_mask = torch.tensor([mask], dtype=torch.long)
    with torch.no_grad():
        outputs = model(input_ids=input_ids, attention_mask=attention_mask)
    label = outputs.logits.argmax(dim=-1).item()
    return "positive" if label == 1 else "negative"

print(predict("Bu ürün gerçekten çok kaliteliydi, kesinlikle tavsiye ederim!"))
# → positive

print(predict("Berbat bir deneyimdi, bir daha almam."))
# → negative

Companion Models

• latensis-roberta-base-tr — Base model
• hecemen-tokenizer-unigram-128k — Tokenizer
• NER model (coming soon)
• STS model (coming soon)

Citation

@misc{latensis2026,
  author    = {Mürşide Aki},
  title     = {Latensis: Turkish NLP Model Suite},
  year      = {2026},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/mursideaki/latensis-sentiment-tr}
}

License

MIT