Upload complete model with all files

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

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+{
+    "word_embedding_dimension": 1024,
+    "pooling_mode_cls_token": true,
+    "pooling_mode_mean_tokens": false,
+    "pooling_mode_max_tokens": false,
+    "pooling_mode_mean_sqrt_len_tokens": false,
+    "pooling_mode_weightedmean_tokens": false,
+    "pooling_mode_lasttoken": false,
+    "include_prompt": true
+}

README.md

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+---
+datasets:
+- newmindai/stsb-deepl-tr
+
+base_model:
+- BAAI/bge-m3
+
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- semantic-textual-similarity
+- turkish
+- multilingual
+- single-task-training
+
+license: apache-2.0
+
+language:
+- tr
+
+metrics:
+- pearson_cosine
+- spearman_cosine
+
+model-index:
+- name: BGE-M3 Turkish STS-B (AnglELoss)
+  results:
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: stsb-eval
+      type: stsb-eval
+    metrics:
+    - type: pearson_cosine
+      value: 0.8575361568991451
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.8629008775002103
+      name: Spearman Cosine
+---
+
+# Turkish Semantic Similarity Model - BGE-M3 (STS-B Fine-tuned)
+
+This is a Turkish semantic textual similarity model fine-tuned from BAAI/bge-m3 on the Turkish STS-B dataset using **AnglELoss** (Angle-optimized Embeddings). The model excels at measuring the semantic similarity between Turkish sentence pairs, achieving state-of-the-art performance on the Turkish STS-B benchmark.
+
+## Overview
+
+* **Base Model**: BAAI/bge-m3 (1024-dimensional embeddings)
+* **Training Task**: Semantic Textual Similarity (STS)
+* **Framework**: Sentence Transformers (v5.1.1)
+* **Language**: Turkish (multilingual base model)
+* **Dataset**: Turkish STS-B (stsb-deepl-tr) - 5,749 training samples
+* **Loss Function**: AnglELoss (Angle-optimized with pairwise angle similarity)
+* **Training Status**: Completed (5 epochs)
+* **Best Checkpoint**: Epoch 1.0 (Step 45) - Validation Loss: 5.682
+* **Final Spearman Correlation**: 86.29%
+* **Final Pearson Correlation**: 85.75%
+* **Context Length**: 1024 tokens
+* **Training Time**: ~8 minutes (single task)
+
+## Performance Metrics
+
+### Final Evaluation Results
+
+**Best Model: Epoch 1.0 (Step 45)**
+
+| Metric | Score |
+|--------|-------|
+| **Spearman Correlation** | **0.8629** (86.29%) |
+| **Pearson Correlation** | **0.8575** (85.75%) |
+| **Validation Loss** | **5.682** |
+
+*Best checkpoint saved at step 45 (epoch 1.0) based on validation loss*
+
+### Training Progression
+
+| Step | Epoch | Training Loss | Validation Loss | Spearman | Pearson |
+|------|-------|---------------|-----------------|----------|---------|
+| 10 | 0.22 | 7.2492 | - | - | - |
+| 15 | 0.33 | - | 6.8784 | 0.8359 | 0.8322 |
+| 30 | 0.67 | 6.0701 | 5.8729 | 0.8340 | 0.8355 |
+| **45** | **1.0** | **-** | **5.682** | **0.8535** | **0.8430** |
+| 60 | 1.33 | 5.5751 | 5.7641 | 0.8572 | 0.8524 |
+| 105 | 2.33 | 5.3594 | 6.0607 | 0.8629 | 0.8551 |
+| 150 | 3.33 | 5.1111 | 6.1735 | 0.8634 | 0.8586 |
+| 165 | 3.67 | - | 6.2597 | 0.8636 | 0.8571 |
+| 225 | 5.0 | - | 6.5089 | 0.8629 | 0.8575 |
+
+*Bold row indicates the best checkpoint selected by early stopping*
+
+## Training Infrastructure
+
+### Hardware Configuration
+
+* **Nodes**: 1
+* **Node Name**: as07r1b16
+* **GPUs per Node**: 4
+* **Total GPUs**: 4
+* **CPUs**: Not specified
+* **Node Hours**: ~0.13 hours (8 minutes)
+* **GPU Type**: NVIDIA (MareNostrum 5 ACC Partition)
+* **Training Type**: Multi-GPU with DataParallel (DP)
+
+### Training Statistics
+
+* **Total Training Steps**: 225
+* **Training Samples**: 5,749 (Turkish STS-B pairs)
+* **Evaluation Samples**: 1,379 (Turkish STS-B pairs)
+* **Final Average Loss**: 5.463
+* **Training Time**: ~6.5 minutes (390 seconds)
+* **Samples/Second**: 73.68
+* **Steps/Second**: 0.577
+
+## Training Configuration
+
+### Batch Configuration
+
+* **Per-Device Batch Size**: 8 (per GPU)
+* **Number of GPUs**: 4
+* **Physical Batch Size**: 32 (4 GPUs × 8 per-device)
+* **Gradient Accumulation Steps**: 4
+* **Effective Batch Size**: 128 (32 physical × 4 accumulation)
+* **Samples per Step**: 32
+
+### Loss Function
+
+* **Type**: AnglELoss (Angle-optimized Embeddings)
+* **Implementation**: Cosine Similarity Loss with angle optimization
+* **Scale**: 20.0 (temperature parameter)
+* **Similarity Function**: pairwise_angle_sim
+* **Task**: Regression (predicting similarity scores 0.0-1.0)
+
+**AnglELoss Advantages**:
+1. **Angle Optimization**: Optimizes the angle between embeddings rather than raw cosine similarity
+2. **Better Geometric Properties**: Encourages uniform distribution on the unit hypersphere
+3. **Improved Discrimination**: Better separation between similar and dissimilar pairs
+4. **Temperature Scaling**: Scale parameter (20.0) controls the sharpness of similarity distribution
+
+### Optimization
+
+* **Optimizer**: AdamW (fused)
+* **Base Learning Rate**: 5e-05
+* **Learning Rate Scheduler**: Linear with warmup
+* **Warmup Steps**: 89
+* **Weight Decay**: 0.01
+* **Max Gradient Norm**: 1.0
+* **Mixed Precision**: Disabled
+
+### Checkpointing & Evaluation
+
+* **Save Strategy**: Every 45 steps
+* **Evaluation Strategy**: Every 15 steps
+* **Logging Steps**: 10
+* **Save Total Limit**: 3 checkpoints
+* **Best Model Selection**: Based on validation loss (lower is better)
+* **Load Best Model at End**: True
+
+## Job Details
+
+| JobID | JobName | Account | Partition | State | Start | End | Node | GPUs | Duration |
+|-------|---------|---------|-----------|-------|-------|-----|------|------|----------|
+| 31478447 | bgem3-base-stsb | ehpc317 | acc | COMPLETED | Nov 3 13:59:58 | Nov 3 14:07:37 | as07r1b16 | 4 | 0.13h |
+
+## Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({
+    'max_seq_length': 1024,
+    'do_lower_case': False,
+    'architecture': 'XLMRobertaModel'
+  })
+  (1): Pooling({
+    'word_embedding_dimension': 1024,
+    'pooling_mode_mean_tokens': True,
+    'pooling_mode_cls_token': False,
+    'pooling_mode_max_tokens': False,
+    'pooling_mode_mean_sqrt_len_tokens': False,
+    'pooling_mode_weightedmean_tokens': False,
+    'pooling_mode_lasttoken': False,
+    'include_prompt': True
+  })
+  (2): Normalize()
+)
+```
+
+## Training Dataset
+
+### stsb-deepl-tr
+
+* **Dataset**: [stsb-deepl-tr](https://huggingface.co/datasets/newmindai/stsb-deepl-tr)
+* **Training Size**: 5,749 sentence pairs
+* **Evaluation Size**: 1,379 sentence pairs
+* **Task**: Semantic Textual Similarity (regression)
+* **Score Range**: 0.0 (completely dissimilar) to 5.0 (semantically equivalent)
+* **Normalized Range**: 0.0 to 1.0 (divided by 5.0 during preprocessing)
+* **Average Sentence Length**: ~10-15 tokens per sentence
+
+### Data Format
+
+Each training example consists of:
+- **Sentence 1**: Turkish sentence (6-30 tokens)
+- **Sentence 2**: Turkish sentence (6-26 tokens)
+- **Similarity Score**: Float value 0.0-1.0 (normalized from 0-5 scale)
+
+### Sample Data
+
+| Sentence 1 | Sentence 2 | Score |
+|:-----------|:-----------|:------|
+| Bir uçak kalkıyor. | Bir uçak havalanıyor. | 0.2 |
+| Bir adam büyük bir flüt çalıyor. | Bir adam flüt çalıyor. | 0.152 |
+| Bir adam pizzanın üzerine rendelenmiş peynir serpiyor. | Bir adam pişmemiş bir pizzanın üzerine rendelenmiş peynir serpiyor. | 0.152 |
+
+## Capabilities
+
+This model is specifically optimized for:
+
+- **Semantic Similarity Scoring**: Predicting similarity scores between Turkish sentence pairs
+- **Paraphrase Detection**: Identifying paraphrases and semantically equivalent sentences
+- **Duplicate Detection**: Finding duplicate or near-duplicate Turkish content
+- **Question-Answer Matching**: Matching questions with semantically similar answers
+- **Document Similarity**: Comparing semantic similarity of Turkish documents
+- **Sentence Clustering**: Grouping semantically similar Turkish sentences
+- **Textual Entailment**: Understanding semantic relationships between sentences
+
+## Usage
+
+### Installation
+
+```bash
+pip install -U sentence-transformers
+```
+
+### Semantic Similarity Scoring
+
+```python
+from sentence_transformers import SentenceTransformer, util
+
+# Load the model
+model = SentenceTransformer("newmindai/bge-m3-stsb-turkish", trust_remote_code=True)
+
+# Turkish sentence pairs
+sentence_pairs = [
+    ["Bir uçak kalkıyor.", "Bir uçak havalanıyor."],
+    ["Bir adam flüt çalıyor.", "Bir kadın zencefil dilimliyor."],
+    ["Bir çocuk sahilde oynuyor.", "Küçük bir çocuk kumda oynuyor."]
+]
+
+# Compute similarity scores
+for sent1, sent2 in sentence_pairs:
+    emb1 = model.encode(sent1, convert_to_tensor=True)
+    emb2 = model.encode(sent2, convert_to_tensor=True)
+    
+    similarity = util.pytorch_cos_sim(emb1, emb2).item()
+    print(f"Similarity: {similarity:.4f}")
+    print(f"  - '{sent1}'")
+    print(f"  - '{sent2}'")
+    print()
+```
+
+### Batch Encoding
+
+```python
+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer("newmindai/bge-m3-stsb-turkish", trust_remote_code=True)
+
+# Turkish sentences
+sentences = [
+    "Bir adam çiftliğinde çalışıyor.",
+    "Yaşlı bir adam çiftliğinde çalışırken bir inek onu tekmeler.",
+    "Bir kedi yavrusu yürüyor.",
+    "İki Hintli kadın sahilde duruyor."
+]
+
+# Encode sentences
+embeddings = model.encode(sentences)
+print(f"Embeddings shape: {embeddings.shape}")
+# Output: (4, 1024)
+
+# Compute similarity matrix
+similarities = model.similarity(embeddings, embeddings)
+print("Similarity matrix:")
+print(similarities)
+```
+
+### Finding Most Similar Sentences
+
+```python
+from sentence_transformers import SentenceTransformer, util
+
+model = SentenceTransformer("newmindai/bge-m3-stsb-turkish", trust_remote_code=True)
+
+# Query and corpus
+query = "Bir adam çiftlikte çalışıyor."
+corpus = [
+    "Yaşlı bir adam çiftliğinde çalışırken bir inek onu tekmeler.",
+    "Bir kedi yavrusu yürüyor.",
+    "Bir kadın kumu kazıyor.",
+    "Kayalık bir deniz kıyısında bir adam ve köpek.",
+    "İki Hintli kadın sahilde iki Hintli kızla birlikte duruyor."
+]
+
+# Encode
+query_emb = model.encode(query, convert_to_tensor=True)
+corpus_emb = model.encode(corpus, convert_to_tensor=True)
+
+# Find most similar
+hits = util.semantic_search(query_emb, corpus_emb, top_k=3)[0]
+
+print(f"Query: {query}\n")
+print("Top 3 most similar sentences:")
+for hit in hits:
+    print(f"{hit['score']:.4f}: {corpus[hit['corpus_id']]}")
+```
+
+## Training Details
+
+### Complete Hyperparameters
+
+| Parameter | Value |
+|-----------|-------|
+| Per-device train batch size | 8 |
+| Number of GPUs | 4 |
+| Physical batch size | 32 |
+| Gradient accumulation steps | 4 |
+| Effective batch size | 128 |
+| Learning rate | 5e-05 |
+| Weight decay | 0.01 |
+| Warmup steps | 89 |
+| LR scheduler | linear |
+| Max gradient norm | 1.0 |
+| Num train epochs | 5 |
+| Save steps | 45 |
+| Eval steps | 15 |
+| Logging steps | 10 |
+| AnglELoss scale | 20.0 |
+| Batch sampler | batch_sampler |
+| Load best model at end | True |
+| Optimizer | adamw_torch_fused |
+
+### Framework Versions
+
+* **Python**: 3.10.12
+* **Sentence Transformers**: 5.1.1
+* **PyTorch**: 2.8.0+cu128
+* **Transformers**: 4.57.0
+* **CUDA**: 12.8
+* **Accelerate**: 1.10.1
+* **Datasets**: 4.2.0
+* **Tokenizers**: 0.22.1
+
+## Use Cases
+
+- **Chatbot Response Matching**: Find the most semantically similar pre-defined response for user queries
+- **FAQ Search**: Match user questions to the most relevant FAQ entries
+- **Content Recommendation**: Recommend articles or documents with similar semantic content
+- **Plagiarism Detection**: Identify semantically similar text for academic integrity checks
+- **Customer Support**: Match support tickets to similar previously resolved issues
+- **Document Clustering**: Group documents by semantic similarity for organization
+- **Paraphrase Mining**: Automatically detect paraphrases in large Turkish text corpora
+- **Semantic Search**: Build semantic search engines for Turkish content
+- **Question Answering**: Match questions to semantically relevant answer candidates
+- **Text Summarization**: Identify redundant sentences for summary generation
+
+## Citation
+
+### AnglELoss
+
+```bibtex
+@inproceedings{li-li-2024-aoe,
+    title = "{A}o{E}: Angle-optimized Embeddings for Semantic Textual Similarity",
+    author = "Li, Xianming and Li, Jing",
+    year = "2024",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2024.acl-long.101/",
+    doi = "10.18653/v1/2024.acl-long.101"
+}
+```
+
+### Sentence Transformers
+
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+### Base Model (BGE-M3)
+
+```bibtex
+@misc{bge-m3,
+    title={BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation},
+    author={Jianlv Chen and Shitao Xiao and Peitian Zhang and Kun Luo and Defu Lian and Zheng Liu},
+    year={2024},
+    eprint={2402.03216},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+### Dataset
+
+```bibtex
+@misc{stsb-deepl-tr,
+    title={Turkish STS-B Dataset (DeepL Translation)},
+    author={NewMind AI},
+    year={2024},
+    url={https://huggingface.co/datasets/newmindai/stsb-deepl-tr}
+}
+```
+
+## License
+
+This model is licensed under the Apache 2.0 License.
+
+## Acknowledgments
+
+* **Base Model**: BAAI/bge-m3
+* **Training Infrastructure**: MareNostrum 5 Supercomputer (Barcelona Supercomputing Center)
+* **Framework**: Sentence Transformers by UKP Lab
+* **Dataset**: [newmindai/stsb-deepl-tr](https://huggingface.co/datasets/newmindai/stsb-deepl-tr)
+* **Loss Function**: AnglELoss (Angle-optimized Embeddings)
+* **Training Approach**: Single-task fine-tuning on Turkish STS-B

config.json

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+  "model_type": "xlm-roberta",
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+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.57.0",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

config_sentence_transformers.json

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+  "__version__": {
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+    "transformers": "4.57.0",
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+  },
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+  "prompts": {
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+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

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+  }
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sentence_bert_config.json

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+    "do_lower_case": false
+}

special_tokens_map.json

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