Name: bert base turkish cased mean nli stsb tr
Author: emrecan

Overview

This model is a specialized BERT-base variant fine-tuned for Turkish Natural Language Inference (NLI) and Semantic Textual Similarity (STS). Unlike generic language models, it is optimized to determine logical relationships between sentence pairs—specifically whether a premise entails, contradicts, or is neutral toward a hypothesis. For developers building Turkish-language applications, this is an ideal tool for automated fact-checking, semantic search, and clustering tasks where understanding nuanced sentence equivalence is critical. It integrates seamlessly into Hugging Face pipelines, offering a robust, cased representation of Turkish text that outperforms standard multilingual models on localized linguistic benchmarks.

Highlights

Optimized for Turkish NLI and semantic similarity tasks
High-precision entailment and contradiction detection
Seamless integration via Hugging Face Transformers
Apache-2.0 license for flexible commercial deployment
Superior performance over general multilingual BERT models

Usage

Install

# Install Hugging Face transformers
pip install transformers torch

SDK Usage

# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("emrecan/bert-base-turkish-cased-mean-nli-stsb-tr")
tokenizer = AutoTokenizer.from_pretrained("emrecan/bert-base-turkish-cased-mean-nli-stsb-tr")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance

pip install -U huggingface_hub

CLI Download

Download the full repository

huggingface-cli download emrecan/bert-base-turkish-cased-mean-nli-stsb-tr

Download a single file to a local folder (e.g. config.json into ./dir)

huggingface-cli download emrecan/bert-base-turkish-cased-mean-nli-stsb-tr config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('emrecan/bert-base-turkish-cased-mean-nli-stsb-tr')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://huggingface.co/emrecan/bert-base-turkish-cased-mean-nli-stsb-tr

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/emrecan/bert-base-turkish-cased-mean-nli-stsb-tr

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

pip install -U transformers torch

Load the model and run inference

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('emrecan/bert-base-turkish-cased-mean-nli-stsb-tr')
tokenizer = AutoTokenizer.from_pretrained('emrecan/bert-base-turkish-cased-mean-nli-stsb-tr')

Full Documentation

来源: HuggingFace

---
language:

tr

pipeline_tag: sentence-similarity
license: apache-2.0
tags:

sentence-transformers

feature-extraction

sentence-similarity

transformers

datasets:

nli_tr

emrecan/stsb-mt-turkish

widget:
source_sentence: "Bu çok mutlu bir kişi"
sentences:
- "Bu mutlu bir köpek"
- "Bu sevincinden havalara uçan bir insan"
- "Çok kar yağıyor"

---

emrecan/bert-base-turkish-cased-mean-nli-stsb-tr

This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. The model was trained on Turkish machine translated versions of NLI and STS-b datasets, using example training scripts from sentence-transformers GitHub repository.

Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:

code

pip install -U sentence-transformers

Then you can use the model like this:

python

from sentence_transformers import SentenceTransformer
sentences = ["Bu örnek bir cümle", "Her cümle vektöre çevriliyor"]
model = SentenceTransformer('emrecan/bert-base-turkish-cased-mean-nli-stsb-tr')
embeddings = model.encode(sentences)
print(embeddings)

Usage (HuggingFace Transformers)

Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.

python

from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
Sentences we want sentence embeddings for
sentences = ["Bu örnek bir cümle", "Her cümle vektöre çevriliyor"]
Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('emrecan/bert-base-turkish-cased-mean-nli-stsb-tr')
model = AutoModel.from_pretrained('emrecan/bert-base-turkish-cased-mean-nli-stsb-tr')
Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
Compute token embeddings
with torch.no_grad():
    model_output = model(encoded_input)
Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)

Evaluation Results

Evaluation results on test and development sets are given below:

| Split | Epoch | cosine_pearson | cosine_spearman | euclidean_pearson | euclidean_spearman | manhattan_pearson | manhattan_spearman | dot_pearson | dot_spearman |
|------------|-------|----------------|-----------------|-------------------|--------------------|-------------------|--------------------|-------------|--------------|
| test | - | 0.834 | 0.830 | 0.820 | 0.819 | 0.819 | 0.818 | 0.799 | 0.789 |
| validation | 1 | 0.850 | 0.848 | 0.831 | 0.835 | 0.83 | 0.83 | 0.80 | 0.806 |
| validation | 2 | 0.857 | 0.857 | 0.844 | 0.848 | 0.844 | 0.848 | 0.813 | 0.810 |
| validation | 3 | 0.860 | 0.859 | 0.846 | 0.851 | 0.846 | 0.850 | 0.825 | 0.822 |
| validation | 4 | 0.859 | 0.860 | 0.846 | 0.851 | 0.846 | 0.851 | 0.825 | 0.823 |

Training
Training scripts training_nli_v2.py and training_stsbenchmark_continue_training.py were used to train the model.
The model was trained with the parameters:

DataLoader:

torch.utils.data.dataloader.DataLoader of length 360 with parameters:
code
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}

Loss**:

sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss

Parameters of the fit()-Method:

code

{
    "epochs": 4,
    "evaluation_steps": 200,
    "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'transformers.optimization.AdamW'>",
    "optimizer_params": {
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 144,
    "weight_decay": 0.01
}

Full Model Architecture

code

SentenceTransformer(
  (0): Transformer({'max_seq_length': 75, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)

Citing & Authors