Turkish Mini Bert Uncased Mean Nli Stsb Tr

🚀 Turkish Mini BERT Uncased Mean NLI STSB TR

This model maps sentences and paragraphs to a 256-dimensional dense vector space, suitable for tasks like clustering and semantic search.

This is a sentence-transformers model. It can transform sentences and paragraphs into a 256-dimensional dense vector space, which can be used for tasks such as clustering or semantic search.

This model is adapted from ytu-ce-cosmos/turkish-mini-bert-uncased and fine - tuned on the following datasets:

• nli_tr
• emrecan/stsb-mt-turkish

⚠️ Important Note

As stated by the model's authors, all texts need to be manually lowercased:

text.replace("I", "ı").lower()

🚀 Quick Start

✨ Features

• Maps sentences and paragraphs to a 256 - dimensional dense vector space.
• Can be used for clustering and semantic search tasks.

📦 Installation

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

pip install -U sentence-transformers

💻 Usage Examples

Basic Usage (Sentence - Transformers)

from sentence_transformers import SentenceTransformer
sentences = ["Bu örnek bir cümle", "Her cümle dönüştürülür"]

model = SentenceTransformer('atasoglu/turkish-mini-bert-uncased-mean-nli-stsb-tr')
embeddings = model.encode(sentences)
print(embeddings)

Advanced 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 dönüştürülür"]

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('atasoglu/turkish-mini-bert-uncased-mean-nli-stsb-tr')
model = AutoModel.from_pretrained('atasoglu/turkish-mini-bert-uncased-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)

📚 Documentation

Evaluation Results

Achieved results on the STS - b test split are given below:

Cosine - Similarity :	    Pearson: 0.8117	Spearman: 0.8074
Manhattan - Distance:	    Pearson: 0.8029	Spearman: 0.7972
Euclidean - Distance:	    Pearson: 0.8028	Spearman: 0.7977
Dot - Product - Similarity:	Pearson: 0.7563	Spearman: 0.7435

Training

The model was trained with the parameters:

DataLoader: torch.utils.data.dataloader.DataLoader of length 45 with parameters:

{'batch_size': 128, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}

Loss: sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss

Parameters of the fit() - Method:

{
    "epochs": 10,
    "evaluation_steps": 4,
    "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
    "optimizer_params": {
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 45,
    "weight_decay": 0.01
}

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 256, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)

📄 License

This model is licensed under the MIT license.

📋 Information Table