Fine-tuned from selmanbaysan/turkish_embedding_model_fine_tuned using a dual-encoder (bi-encoder) architecture with in-batch contrastive loss on Turkish QA pairs.

Encoders

Repo	Role
`sfidan42/turkish_embedding_fine_tuned_q_enc`	Query encoder
`sfidan42/turkish_embedding_fine_tuned_a_enc`	Answer / passage encoder

Training details

Loss: In-batch cross-entropy (InfoNCE), temperature=0.1
Optimizer: AdamW, lr=5e-6, gradient clipping=1.0
Train sets: sfidan42/Quora_QA_Turkish_First_Answers, sfidan42/Quora_QA_Turkish_Second_Answers
Eval set: sfidan42/Turkish-QA-Ranked

Usage

python

from transformers import AutoTokenizer, AutoModel
import torch

tokenizer = AutoTokenizer.from_pretrained("sfidan42/turkish_embedding_fine_tuned_q_enc")
model     = AutoModel.from_pretrained("sfidan42/turkish_embedding_fine_tuned_q_enc")

def embed(texts):
    enc = tokenizer(texts, padding=True, truncation=True, max_length=128, return_tensors="pt")
    with torch.no_grad():
        o = model(**enc).last_hidden_state
        m = enc["attention_mask"].unsqueeze(-1)
        return (o * m).sum(1) / m.sum(1)  # mean pooling

query_vec   = embed(["Türkiye'nin başkenti neresidir?"])
passage_vec = embed(["Türkiye'nin başkenti Ankara'dır."])
score = torch.nn.functional.cosine_similarity(query_vec, passage_vec)