mursideaki/latensis-sentiment-tr

Latensis Sentiment — Turkish Sentiment Analysis

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

ID	Label
0	Negative
1	Positive

Benchmark Results

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

Dataset	Our Model	BERTurk	Notes
TRSAv1 — Accuracy	0.9027	0.8372	Independent test set
TRSAv1 — F1-macro	0.9026	0.8350	Independent test set
Winvoker — Accuracy	0.8534	0.6944	BERTurk trained on this data
Winvoker — F1-macro	0.7862	0.6488	BERTurk trained on this data
SentiTurca Movies — Accuracy	0.8060	0.8186	Film domain

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

Property	Value
Base model	Latensis RoBERTa Base (500k steps, val loss 3.21)
Training data	19k examples (film + product reviews)
Labels	Binary (positive / negative)
Tokenizer	Hecemen Unigram 128k

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