---
language:
- tr
license: cc-by-sa-4.0
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- embeddings
- turkish
- türkçe
- e5
- retrieval
- semantic-search
- mteb
- tr-mteb
base_model: intfloat/multilingual-e5-base
datasets:
- mertcobanov/all-nli-triplets-turkish
- emrecan/stsb-mt-turkish
metrics:
- spearmanr
- ndcg
- accuracy
- v_measure
model-index:
- name: e5-tr-nli
  results:
  - task:
      type: semantic-textual-similarity
      name: Semantic Textual Similarity
    dataset:
      name: STSb-TR (test)
      type: trmteb/stsb-tr
    metrics:
    - type: spearman_cosine
      value: 0.7907
  - task:
      type: retrieval
      name: Retrieval
    dataset:
      name: TQuad
      type: trmteb/tquad
    metrics:
    - type: ndcg_at_10
      value: 0.8222
  - task:
      type: retrieval
      name: Retrieval
    dataset:
      name: Quora-TR
      type: trmteb/quora-tr
    metrics:
    - type: ndcg_at_10
      value: 0.7323
  - task:
      type: retrieval
      name: Retrieval
    dataset:
      name: MS MARCO-TR
      type: trmteb/msmarco-tr
    metrics:
    - type: ndcg_at_10
      value: 0.2734
  - task:
      type: classification
      name: Classification
    dataset:
      name: News-Cat
      type: trmteb/news-cat
    metrics:
    - type: accuracy
      value: 0.9600
---

# e5-tr-nli — Turkish Sentence Embedding Model (Bi-Encoder)

A **Turkish sentence-embedding (bi-encoder) model** for **retrieval and semantic
search**, fine-tuned from [`intfloat/multilingual-e5-base`](https://huggingface.co/intfloat/multilingual-e5-base)
with contrastive learning (`MultipleNegativesRankingLoss`) on Turkish NLI triplets.

- **Embedding dimension:** 768
- **Max sequence length:** 256 tokens (trained); backbone supports 512
- **Pooling:** mean pooling
- **Similarity function:** cosine
- **Parameters:** ~278M (XLM-RoBERTa-base backbone)
- **Prefix convention:** e5-style `query:` / `passage:` (see [Usage](#usage) — **required**)

---

## Table of Contents
1. [Intended Use](#intended-use)
2. [Usage](#usage)
3. [Training Data](#training-data)
4. [Training Procedure](#training-procedure)
5. [Evaluation](#evaluation)
6. [TR-MTEB Results](#tr-mteb-results-full)
7. [Limitations & Biases](#limitations--biases)
8. [Compute & Environmental Impact](#compute--environmental-impact)
9. [Licensing](#licensing)
10. [Citation](#citation)
11. [Reproduction](#reproduction)

---

## Intended Use

**In scope**
- Turkish semantic search / passage retrieval (dense retrieval, RAG)
- Semantic textual similarity, paraphrase & duplicate detection
- Clustering and topic grouping of Turkish text
- Feature extraction for downstream Turkish NLP classifiers

**Out of scope**
- Re-ranking with query–document cross-attention (use a cross-encoder instead)
- Long-document embedding beyond 256 tokens without chunking
- High-stakes decisions (legal, medical, hiring) without human oversight
- Non-Turkish text (backbone is multilingual, but this model is tuned for Turkish)

---

## Usage

### ⚠️ e5 prefixes are mandatory
The model was trained with the e5 asymmetric prefixes and **must** be used with them,
consistently at training / evaluation / inference:

| Role | Prefix |
|------|--------|
| Query / anchor / any symmetric-task sentence (STS, classification, clustering) | `query: ` |
| Passage / document / positive & negative candidates | `passage: ` |

Omitting the prefixes degrades quality noticeably.

### With `sentence-transformers`
```python
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim

model = SentenceTransformer("thealper2/intfloat-multilingual-e5-base-tr-nli")

# Asymmetric retrieval: query vs. candidate passages
query = "query: Türkiye'nin başkenti neresidir?"
passages = [
    "passage: Ankara, Türkiye'nin başkentidir.",
    "passage: İstanbul Türkiye'nin en kalabalık şehridir.",
    "passage: Muz tropikal bir meyvedir.",
]
q = model.encode(query, normalize_embeddings=True)
p = model.encode(passages, normalize_embeddings=True)
print(cos_sim(q, p))   # highest score -> the Ankara passage
```

### Semantic similarity (symmetric — use `query:` on both sides)
```python
a = model.encode("query: Bugün hava çok güzel.", normalize_embeddings=True)
b = model.encode("query: Hava bugün oldukça güzel.", normalize_embeddings=True)
print(float(cos_sim(a, b)))   # ~0.9
```

### With 🤗 Transformers (mean pooling)
```python
import torch, torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel

tok = AutoTokenizer.from_pretrained("thealper2/intfloat-multilingual-e5-base-tr-nli")
mdl = AutoModel.from_pretrained("thealper2/intfloat-multilingual-e5-base-tr-nli")

def encode(texts):
    batch = tok(texts, padding=True, truncation=True, max_length=256, return_tensors="pt")
    with torch.no_grad():
        out = mdl(**batch)
    mask = batch["attention_mask"].unsqueeze(-1).float()
    emb = (out.last_hidden_state * mask).sum(1) / mask.sum(1)   # mean pooling
    return F.normalize(emb, p=2, dim=1)

emb = encode(["query: örnek cümle", "passage: örnek pasaj"])
```

---

## Training Data

- **Source:** [`mertcobanov/all-nli-triplets-turkish`](https://huggingface.co/datasets/mertcobanov/all-nli-triplets-turkish)
  — a machine-translated Turkish version of the AllNLI (SNLI + MultiNLI) triplet set.
- **Format:** `(anchor, positive, negative)` triplets, where the `negative` acts as a
  **hard negative** for the contrastive objective.
- **Column handling:** **Only the Turkish columns** (`anchor_translated`,
  `positive_translated`, `negative_translated`) were used and renamed to
  `anchor` / `positive` / `negative`. **All English columns were discarded.**
- **Cleaning:** rows with `None` / empty / whitespace-only fields were filtered out.
- **Resulting sizes (after filtering):**

  | Split | Triplets |
  |-------|---------:|
  | train | **277,167** |
  | dev   | 6,584 |
  | test  | 6,609 |

---

## Training Procedure

### Objective
- **Loss:** `CachedMultipleNegativesRankingLoss` (MNRL with in-batch + hard negatives;
  the cached variant is used to simulate a large effective batch on limited VRAM).
- **Batch sampler:** `NO_DUPLICATES` (required for MNRL to avoid trivial in-batch collisions).
- **In-batch negatives:** every other positive/negative in the batch serves as a negative
  for a given anchor, so larger batches yield a stronger contrastive signal.

### Hyperparameter search
An **Optuna** search (TPE sampler) was run **before** the final training:

| Setting | Value |
|--------|-------|
| Trials | 10 |
| Subset | 40,000 random training triplets |
| Steps / trial | 600 |
| Search space | `lr ∈ {1e-5, 2e-5, 3e-5}`, `batch ∈ {32, 64, 128}`, `warmup_ratio ∈ {0.0, 0.1}` |
| Selection metric | `stsb-tr` **dev** `spearman_cosine` |
| **Best config** | **lr = 2e-5, batch = 32, warmup_ratio = 0.1** (dev spearman = **0.8285**) |

### Final training

| Setting | Value |
|--------|-------|
| Base model | `intfloat/multilingual-e5-base` |
| Epochs | 1 |
| Learning rate | 2e-5 |
| Batch size | 32 (cached MNRL) |
| Warmup ratio | 0.1 |
| Max sequence length | 256 |
| Precision | bf16 |
| Seed | 42 |
| Eval strategy | steps, monitoring `eval_stsb-tr-dev_spearman_cosine` |
| Checkpoint selection | `load_best_model_at_end=True` (best dev spearman) |

### Frameworks & hardware
- `sentence-transformers` 5.2.2, `transformers` 5.0.0, `torch` 2.11.0+cu128,
  `datasets` 4.4.1, Python 3.12
- 1× NVIDIA GeForce RTX 5050 Laptop GPU (8 GB), CUDA 12.8

---

## Evaluation

### Baseline (base model) vs. Final (this model)
Measured on the dataset's own test triplets (`TripletEvaluator`, cosine accuracy) and
on the external Turkish STS set [`emrecan/stsb-mt-turkish`](https://huggingface.co/datasets/emrecan/stsb-mt-turkish)
(`EmbeddingSimilarityEvaluator`, Spearman; scores normalised 0–5 → 0–1):

| Metric | Baseline (e5-base) | Final | Δ |
|--------|:---:|:---:|:---:|
| stsb-tr **dev** spearman_cosine | 0.8092 | **0.8312** | +0.0220 |
| stsb-tr **test** spearman_cosine | 0.7761 | **0.7814** | +0.0053 |
| nli-tr **test** cosine_accuracy | 0.8968 | **0.9260** | +0.0292 |

### Methodology notes for TR-MTEB
- Datasets pulled **directly from HuggingFace** (`trmteb/*`); metrics computed with a
  standalone script (no `mteb` package dependency), following standard MTEB protocols:
  - **STS** → Spearman of cosine vs. gold; **Retrieval** → nDCG@10 / Recall@10 / MAP@10
    via exact cosine search; **Classification** → logistic regression on frozen
    embeddings (accuracy + macro-F1); **PairClassification** → average precision of cosine;
    **Clustering** → V-measure (MiniBatchKMeans, k = #labels); **BitextMining** → top-1
    nearest-neighbour accuracy.
- e5 prefixes applied throughout (`query:` for queries/symmetric sides, `passage:` for corpus).
- Retrieval search runs on-GPU in fp16.
- `75haber`, `thy_sa`, `irony-tr` have **no train split** in the `trmteb` org, so a seeded
  **70/30 stratified self-split** was used for those classification tasks (marked `note` in
  `trmteb_results.json`). All other classification sets use their official train/test.

### Category summary (macro averages)

| Task type | # datasets | Avg. main metric |
|-----------|:---:|:---:|
| STS (spearman_cosine) | 1 | **0.791** |
| BitextMining (accuracy) | 1 | **0.975** |
| Classification (accuracy) | 6 | **0.817** |
| PairClassification (AP) | 3 | **0.573** |
| Clustering (v-measure) | 2 | **0.487** |
| Retrieval (nDCG@10) | 10 | **0.407** |
| **Overall (mean of category means)** | 23 | **0.675** |

---

## TR-MTEB Results (full)

Evaluated on the [TR-MTEB](https://huggingface.co/trmteb) datasets
(Baysan & Güngör, *TR-MTEB*, Findings of EMNLP 2025).

| Task type | Dataset | Main metric | Value |
|---|---|---|---:|
| STS | stsb-tr | spearman_cosine | 0.7907 |
| BitextMining | wmt16_en_tr | accuracy | 0.9753 |
| Classification | news-cat | accuracy | 0.9600 |
| Classification | 75haber ᵃ | accuracy | 0.8696 |
| Classification | ts_timeline_news_category | accuracy | 0.7250 |
| Classification | thy_sa ᵃ | accuracy | 0.8386 |
| Classification | offenseval | accuracy | 0.8427 |
| Classification | irony-tr ᵃ | accuracy | 0.6667 |
| PairClassification | snli_tr | ap_cosine | 0.5280 |
| PairClassification | multinli_tr | ap_cosine | 0.6324 |
| PairClassification | xnli_tr | ap_cosine | 0.5591 |
| Clustering | ts_abstract_corpus | v_measure | 0.5762 |
| Clustering | 630koseyazisi | v_measure | 0.3978 |
| Retrieval | tquad | ndcg@10 | 0.8222 |
| Retrieval | quora-tr | ndcg@10 | 0.7323 |
| Retrieval | squad-tr | ndcg@10 | 0.6584 |
| Retrieval | scifact-tr | ndcg@10 | 0.5309 |
| Retrieval | cqadupstack-gaming-tr | ndcg@10 | 0.3584 |
| Retrieval | msmarco-tr | ndcg@10 | 0.2734 |
| Retrieval | arguana-tr | ndcg@10 | 0.2583 |
| Retrieval | nfcorpus-tr | ndcg@10 | 0.2272 |
| Retrieval | fiqa-tr | ndcg@10 | 0.1237 |
| Retrieval | scidocs-tr | ndcg@10 | 0.0887 |

---

## Limitations & Biases
- **Translated training data.** The NLI triplets are machine-translated; translation
  artefacts and noise are inherited, which can cap fine-grained semantic precision.
- **Short-text bias.** Trained on NLI-style short sentences at 256 tokens; long-document
  retrieval requires chunking and may underperform (see the lower `fiqa`/`scidocs` scores).
- **Prefix sensitivity.** Using the model without `query:`/`passage:` prefixes degrades results.
- **Domain gaps.** Scientific/financial retrieval (scidocs, fiqa) is weak; the model is
  strongest on general-domain QA-style retrieval (tquad, squad-tr, quora-tr).
- **Societal bias.** The backbone and NLI data may encode social biases; validate before
  deployment in sensitive settings.
- **Single epoch.** Trained for 1 epoch; longer schedules or larger effective batches may
  improve retrieval further.

## Compute & Environmental Impact
- Single consumer laptop GPU (RTX 5050, 8 GB). Full pipeline (Optuna sweep + 1-epoch
  training on 277k triplets + evaluation) ran in a few GPU-hours. No multi-GPU/cluster
  training was used.

## Licensing
- **Backbone** `intfloat/multilingual-e5-base`: MIT.
- **Training data** derives from **AllNLI** (SNLI is **CC BY-SA 4.0**; MultiNLI has mixed
  source licenses), translated to Turkish. Because the most restrictive component is
  CC BY-SA 4.0, this model card is released under **CC BY-SA 4.0**; verify that this suits
  your use case and comply with the ShareAlike terms.
- **Evaluation datasets** belong to their respective authors (see the `trmteb` org and
  `emrecan/stsb-mt-turkish`).

## Citation

This model:
```bibtex
@misc{e5-tr-nli,
  title  = {e5-tr-nli: A Turkish Sentence Embedding Model},
  note   = {Fine-tuned from intfloat/multilingual-e5-base on Turkish NLI triplets},
  year   = {2026}
}
```

TR-MTEB benchmark:
```bibtex
@inproceedings{baysan-gungor-2025-trmteb,
  title     = {{TR-MTEB}: A Comprehensive Benchmark and Embedding Model Suite for {T}urkish Sentence Representations},
  author    = {Baysan, Mehmet Selman and G{\"u}ng{\"o}r, Tunga},
  booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2025},
  year      = {2025}
}
```

Base model (E5):
```bibtex
@article{wang2024multilingual,
  title   = {Multilingual E5 Text Embeddings: A Technical Report},
  author  = {Wang, Liang and Yang, Nan and Huang, Xiaolong and Yang, Linjun and Majumder, Rangan and Wei, Furu},
  journal = {arXiv preprint arXiv:2402.05672},
  year    = {2024}
}
```

## Reproduction
The full pipeline (data prep, Optuna sweep, training, and TR-MTEB evaluation) is scripted:
- `train_embedding_tr.py` — `--mode sweep | train | eval | mteb`
- `eval_trmteb_hf.py` — TR-MTEB evaluation straight from HuggingFace `trmteb/*` datasets
- `config.py`, `Makefile`, `requirements.txt`

To publish (disabled by default — no secrets are used):
```python
from sentence_transformers import SentenceTransformer
SentenceTransformer("models/e5-tr-nli-final").push_to_hub("thealper2/intfloat-multilingual-e5-base-tr-nli")
```