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---
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language:
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- en
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license: apache-2.0
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tags:
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- sentence-transformers
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- sentence-similarity
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- feature-extraction
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- generated_from_trainer
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- dataset_size:13842
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- loss:MultipleNegativesRankingLoss
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base_model: microsoft/mpnet-base
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widget:
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- source_sentence: Bir köpek sahibi, evcil hayvanıyla birlikte koşuyor ve evcil hayvan
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bir parkurda engellerden kaçınıyor.
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sentences:
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- Bazı bitkilerin önünde mavi bir kano.
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- Bir adam köpeğinin yanında koşuyor.
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- Adam bir kediyle birlikte.
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- source_sentence: Parlamenter bölümünün patronunun ev hizmetiyle bağlantılı bir politikacı,
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0-609-3459812 numaralı cep telefonuna sahip ve mizah anlayışının olmamasıyla tanınıyor,
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'Hayran' adlı birinden gelen 'En iyi kürek dilekleri' mesajını pek iyi karşılamadı.
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sentences:
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- Doktor Perennial, kötü niyetli çavuş uyandığında ayakta duruyordu.
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- Politikacı, patronunun ev hizmetini aradığında, bir 'hayran'dan gelen bir mesaja
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pek hoş karşılamadı.
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- Mesajı aldığı için o kadar minnettardı ki, gönderen kişiye bir demet çiçek gönderdi.
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- source_sentence: Bankanın kasalarında.
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sentences:
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- Ayakta duran bir insan
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- Banka kasasında.
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- Bankadaki kasa.
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- source_sentence: Bir grup Asyalı erkek, birlikte bir yemek yedikten sonra büyük
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bir masanın etrafında poz veriyor.
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sentences:
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- Bir grup Asyalı erkek birlikte bir yemek yedi.
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- Pazarlar, kaplıcalar ve kayak pistleri burada bulunan diğer cazibe merkezlerinden
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bazılarını oluşturuyor.
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- Bir grup Asyalı erkek futbol oynuyor.
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- source_sentence: Böyle şeyler görmek ve eğer yapabileceğiniz en küçük bir şey varsa,
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bu yardımcı olur.
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sentences:
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- Böyle bir şeyi gözlemlemek ve yapıp yapamayacağınızı bilmek için.
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- Adamın gömleği, kot pantolonundan farklı bir renkte.
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- Böyle bir şeyi görmek kötü, eğer yapabiliyorsanız buna hiç katkıda bulunmayın.
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datasets:
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- mertcobanov/all-nli-triplets-turkish
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pipeline_tag: sentence-similarity
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library_name: sentence-transformers
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metrics:
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- cosine_accuracy
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model-index:
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- name: MPNet base trained on AllNLI-turkish triplets
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results:
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- task:
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type: triplet
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name: Triplet
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dataset:
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name: all nli dev turkish
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type: all-nli-dev-turkish
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metrics:
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- type: cosine_accuracy
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value: 0.7454434993924666
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name: Cosine Accuracy
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- task:
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type: triplet
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name: Triplet
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dataset:
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name: all nli test turkish
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type: all-nli-test-turkish
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metrics:
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- type: cosine_accuracy
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value: 0.7524587683461946
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name: Cosine Accuracy
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---
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# MPNet base trained on AllNLI-turkish triplets
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) on the [all-nli-triplets-turkish](https://huggingface.co/datasets/mertcobanov/all-nli-triplets-turkish) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
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## Model Details
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### Model Description
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- **Model Type:** Sentence Transformer
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- **Base model:** [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) <!-- at revision 6996ce1e91bd2a9c7d7f61daec37463394f73f09 -->
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- **Maximum Sequence Length:** 512 tokens
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- **Output Dimensionality:** 768 dimensions
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- **Similarity Function:** Cosine Similarity
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- **Training Dataset:**
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- [all-nli-triplets-turkish](https://huggingface.co/datasets/mertcobanov/all-nli-triplets-turkish)
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- **Language:** en
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- **License:** apache-2.0
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### Model Sources
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
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### Full Model Architecture
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```
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SentenceTransformer(
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
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(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, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
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)
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```
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## Usage
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### Direct Usage (Sentence Transformers)
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First install the Sentence Transformers library:
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```bash
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pip install -U sentence-transformers
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```
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Then you can load this model and run inference.
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```python
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from sentence_transformers import SentenceTransformer
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# Download from the 🤗 Hub
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model = SentenceTransformer("mertcobanov/mpnet-base-all-nli-triplet-turkish-v2")
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# Run inference
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sentences = [
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'Böyle şeyler görmek ve eğer yapabileceğiniz en küçük bir şey varsa, bu yardımcı olur.',
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'Böyle bir şeyi gözlemlemek ve yapıp yapamayacağınızı bilmek için.',
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'Böyle bir şeyi görmek kötü, eğer yapabiliyorsanız buna hiç katkıda bulunmayın.',
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]
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embeddings = model.encode(sentences)
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print(embeddings.shape)
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# [3, 768]
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# Get the similarity scores for the embeddings
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similarities = model.similarity(embeddings, embeddings)
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print(similarities.shape)
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# [3, 3]
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```
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<!--
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### Direct Usage (Transformers)
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<details><summary>Click to see the direct usage in Transformers</summary>
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</details>
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-->
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<!--
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### Downstream Usage (Sentence Transformers)
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You can finetune this model on your own dataset.
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<details><summary>Click to expand</summary>
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</details>
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-->
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<!--
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### Out-of-Scope Use
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*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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-->
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## Evaluation
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### Metrics
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#### Triplet
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* Datasets: `all-nli-dev-turkish` and `all-nli-test-turkish`
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* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
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| Metric | all-nli-dev-turkish | all-nli-test-turkish |
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|:--------------------|:--------------------|:---------------------|
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| **cosine_accuracy** | **0.7454** | **0.7525** |
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<!--
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## Bias, Risks and Limitations
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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-->
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<!--
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### Recommendations
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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-->
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## Training Details
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### Training Dataset
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#### all-nli-triplets-turkish
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* Dataset: [all-nli-triplets-turkish](https://huggingface.co/datasets/mertcobanov/all-nli-triplets-turkish) at [bff203b](https://huggingface.co/datasets/mertcobanov/all-nli-triplets-turkish/tree/bff203b01bbf5b818f7ad85be0adbe8d64eba9ee)
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* Size: 13,842 training samples
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* Columns: <code>anchor_translated</code>, <code>positive_translated</code>, and <code>negative_translated</code>
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* Approximate statistics based on the first 1000 samples:
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| | anchor_translated | positive_translated | negative_translated |
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|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
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| type | string | string | string |
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| details | <ul><li>min: 8 tokens</li><li>mean: 13.42 tokens</li><li>max: 95 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 31.64 tokens</li><li>max: 93 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 32.03 tokens</li><li>max: 89 tokens</li></ul> |
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* Samples:
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| anchor_translated | positive_translated | negative_translated |
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|:-----------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|
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| <code>Asyalı okul çocukları birbirlerinin omuzlarında oturuyor.</code> | <code>Okul çocukları bir arada</code> | <code>Asyalı fabrika işçileri oturuyor.</code> |
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| <code>İnsanlar dışarıda.</code> | <code>Arka planda resmi kıyafetler giymiş bir grup insan var ve beyaz gömlekli, haki pantolonlu bir adam toprak yoldan yeşil çimenlere atlıyor.</code> | <code>Bir odada üç kişiyle birlikte büyük bir kamera tutan bir adam.</code> |
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| <code>Bir adam dışarıda.</code> | <code>Adam yarış sırasında yan sepetten bir su birikintisine düşer.</code> | <code>Beyaz bir sarık sarmış gömleksiz bir adam bir ağaç gövdesine tırmanıyor.</code> |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
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```json
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{
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"scale": 20.0,
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"similarity_fct": "cos_sim"
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}
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```
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### Evaluation Dataset
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#### all-nli-triplets-turkish
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* Dataset: [all-nli-triplets-turkish](https://huggingface.co/datasets/mertcobanov/all-nli-triplets-turkish) at [bff203b](https://huggingface.co/datasets/mertcobanov/all-nli-triplets-turkish/tree/bff203b01bbf5b818f7ad85be0adbe8d64eba9ee)
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* Size: 6,584 evaluation samples
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* Columns: <code>anchor_translated</code>, <code>positive_translated</code>, and <code>negative_translated</code>
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* Approximate statistics based on the first 1000 samples:
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| | anchor_translated | positive_translated | negative_translated |
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|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
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| type | string | string | string |
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| details | <ul><li>min: 5 tokens</li><li>mean: 42.62 tokens</li><li>max: 192 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 22.58 tokens</li><li>max: 77 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 22.07 tokens</li><li>max: 65 tokens</li></ul> |
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* Samples:
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| anchor_translated | positive_translated | negative_translated |
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|:--------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------|
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| <code>Ayrıca, bu özel tüketim vergileri, diğer vergiler gibi, hükümetin ödeme zorunluluğunu sağlama yetkisini kullanarak belirlenir.</code> | <code>Hükümetin ödeme zorlaması, özel tüketim vergilerinin nasıl hesaplandığını belirler.</code> | <code>Özel tüketim vergileri genel kuralın bir istisnasıdır ve aslında GSYİH payına dayalı olarak belirlenir.</code> |
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| <code>Gri bir sweatshirt giymiş bir sanatçı, canlı renklerde bir kasaba tablosu üzerinde çalışıyor.</code> | <code>Bir ressam gri giysiler içinde bir kasabanın resmini yapıyor.</code> | <code>Bir kişi bir beyzbol sopası tutuyor ve gelen bir atış için planda bekliyor.</code> |
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| <code>İmkansız.</code> | <code>Yapılamaz.</code> | <code>Tamamen mümkün.</code> |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
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```json
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{
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"scale": 20.0,
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"similarity_fct": "cos_sim"
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}
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```
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### Training Hyperparameters
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#### Non-Default Hyperparameters
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- `eval_strategy`: steps
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- `per_device_train_batch_size`: 16
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- `per_device_eval_batch_size`: 16
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- `learning_rate`: 2e-05
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- `num_train_epochs`: 1
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- `warmup_ratio`: 0.1
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- `fp16`: True
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- `batch_sampler`: no_duplicates
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#### All Hyperparameters
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<details><summary>Click to expand</summary>
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- `overwrite_output_dir`: False
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- `do_predict`: False
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- `eval_strategy`: steps
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- `prediction_loss_only`: True
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- `per_device_train_batch_size`: 16
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- `per_device_eval_batch_size`: 16
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- `per_gpu_train_batch_size`: None
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- `per_gpu_eval_batch_size`: None
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- `gradient_accumulation_steps`: 1
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- `eval_accumulation_steps`: None
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- `torch_empty_cache_steps`: None
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- `learning_rate`: 2e-05
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- `weight_decay`: 0.0
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- `adam_beta1`: 0.9
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- `adam_beta2`: 0.999
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- `adam_epsilon`: 1e-08
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- `max_grad_norm`: 1.0
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- `num_train_epochs`: 1
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- `max_steps`: -1
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- `lr_scheduler_type`: linear
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- `lr_scheduler_kwargs`: {}
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- `warmup_ratio`: 0.1
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- `warmup_steps`: 0
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- `log_level`: passive
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- `log_level_replica`: warning
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- `log_on_each_node`: True
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- `logging_nan_inf_filter`: True
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- `save_safetensors`: True
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- `save_on_each_node`: False
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- `save_only_model`: False
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- `restore_callback_states_from_checkpoint`: False
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- `no_cuda`: False
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- `use_cpu`: False
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- `use_mps_device`: False
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- `seed`: 42
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- `data_seed`: None
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- `jit_mode_eval`: False
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- `use_ipex`: False
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- `bf16`: False
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- `fp16`: True
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- `fp16_opt_level`: O1
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- `half_precision_backend`: auto
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- `bf16_full_eval`: False
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- `fp16_full_eval`: False
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- `tf32`: None
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- `local_rank`: 0
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- `ddp_backend`: None
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- `tpu_num_cores`: None
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- `tpu_metrics_debug`: False
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- `debug`: []
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- `dataloader_drop_last`: False
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- `dataloader_num_workers`: 0
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- `dataloader_prefetch_factor`: None
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- `past_index`: -1
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- `disable_tqdm`: False
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- `remove_unused_columns`: True
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- `label_names`: None
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- `load_best_model_at_end`: False
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- `ignore_data_skip`: False
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- `fsdp`: []
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- `fsdp_min_num_params`: 0
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
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- `fsdp_transformer_layer_cls_to_wrap`: None
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
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- `deepspeed`: None
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- `label_smoothing_factor`: 0.0
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- `optim`: adamw_torch
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- `optim_args`: None
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- `adafactor`: False
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- `group_by_length`: False
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- `length_column_name`: length
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- `ddp_find_unused_parameters`: None
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- `ddp_bucket_cap_mb`: None
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- `ddp_broadcast_buffers`: False
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- `dataloader_pin_memory`: True
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- `dataloader_persistent_workers`: False
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- `skip_memory_metrics`: True
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- `use_legacy_prediction_loop`: False
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- `push_to_hub`: False
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- `resume_from_checkpoint`: None
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- `hub_model_id`: None
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- `hub_strategy`: every_save
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- `hub_private_repo`: False
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- `hub_always_push`: False
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- `gradient_checkpointing`: False
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- `gradient_checkpointing_kwargs`: None
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- `include_inputs_for_metrics`: False
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- `include_for_metrics`: []
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- `eval_do_concat_batches`: True
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- `fp16_backend`: auto
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- `push_to_hub_model_id`: None
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- `push_to_hub_organization`: None
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- `mp_parameters`:
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- `auto_find_batch_size`: False
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- `full_determinism`: False
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- `torchdynamo`: None
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- `ray_scope`: last
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- `ddp_timeout`: 1800
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- `torch_compile`: False
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- `torch_compile_backend`: None
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- `torch_compile_mode`: None
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- `dispatch_batches`: None
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- `split_batches`: None
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- `include_tokens_per_second`: False
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- `include_num_input_tokens_seen`: False
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- `neftune_noise_alpha`: None
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- `optim_target_modules`: None
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- `batch_eval_metrics`: False
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- `eval_on_start`: False
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- `use_liger_kernel`: False
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- `eval_use_gather_object`: False
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- `average_tokens_across_devices`: False
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- `prompts`: None
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- `batch_sampler`: no_duplicates
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- `multi_dataset_batch_sampler`: proportional
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</details>
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### Training Logs
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| Epoch | Step | Training Loss | Validation Loss | all-nli-dev-turkish_cosine_accuracy | all-nli-test-turkish_cosine_accuracy |
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|:------:|:----:|:-------------:|:---------------:|:-----------------------------------:|:------------------------------------:|
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| 0 | 0 | - | - | 0.6092 | - |
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| 0.1155 | 100 | 2.7414 | 1.6615 | 0.7429 | - |
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| 0.2309 | 200 | 1.64 | 1.4650 | 0.7483 | - |
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| 0.3464 | 300 | 1.2391 | 1.4068 | 0.7561 | - |
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| 0.4619 | 400 | 1.1146 | 1.4367 | 0.7549 | - |
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| 0.5774 | 500 | 1.0341 | 1.4887 | 0.7486 | - |
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| 0.6928 | 600 | 0.7568 | 1.4568 | 0.7535 | - |
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| 0.8083 | 700 | 0.7216 | 1.5680 | 0.7451 | - |
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| 0.9238 | 800 | 0.5919 | 1.5492 | 0.7454 | - |
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| 1.0 | 866 | - | - | - | 0.7525 |
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### Framework Versions
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- Python: 3.10.14
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- Sentence Transformers: 3.3.1
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- Transformers: 4.46.3
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- PyTorch: 2.3.0
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- Accelerate: 1.1.1
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- Datasets: 3.1.0
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- Tokenizers: 0.20.3
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## Citation
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### BibTeX
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#### Sentence Transformers
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```bibtex
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@inproceedings{reimers-2019-sentence-bert,
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
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author = "Reimers, Nils and Gurevych, Iryna",
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
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month = "11",
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year = "2019",
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publisher = "Association for Computational Linguistics",
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url = "https://arxiv.org/abs/1908.10084",
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}
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```
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#### MultipleNegativesRankingLoss
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```bibtex
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@misc{henderson2017efficient,
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title={Efficient Natural Language Response Suggestion for Smart Reply},
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author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
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year={2017},
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eprint={1705.00652},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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