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:

pip install -U sentence-transformers

Then you can use the model like this:

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)

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.

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:")

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 scripts training_nli_v2.py and training_stsbenchmark_continue_training.py were used to train the model.

The model was trained with the parameters:


torch.utils.data.dataloader.DataLoader of length 360 with parameters:

{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}



Parameters of the fit()-Method:

    "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

  (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

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This model can be loaded on the Inference API on-demand.

Datasets used to train emrecan/bert-base-turkish-cased-mean-nli-stsb-tr