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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 adam bir elinde kahve fincanı, diğer elinde tuvalet fırçası
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ile tuvaletin önünde duruyor.
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sentences:
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- Şef ve orkestra oturmuyor.
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- Bir adam bir banyoda duruyor.
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- Bir adam kahve demlemeye çalışıyor.
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- source_sentence: Sarı ceketli ve siyah pantolonlu iki adam madalyalara sahip.
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sentences:
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- Erkeklere bir noktada bir ödül verilmiştir.
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- Başlangıçtaki net ölçek faydası, ücret primleri olsun ya da olmasın, pozitiftir.
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- Adamlar düz kırmızı ceketler ve mavi pantolonlar giymiş.
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- source_sentence: 'Restoran zinciri içi: Planet Hollywood, çeşitli film hatıraları
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mekânı süslüyor.'
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sentences:
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- Kadın bir şey tutuyor.
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- Bir restoranın içi.
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- Yeni gümüş makinelerin bulunduğu bir çamaşırhane içi.
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- source_sentence: İki çocuk, binanın yakınındaki kaldırımda sokakta koşuyor.
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sentences:
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- Çocuklar dışarıda.
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- Bazı odaların dışına balkonları vardır.
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- Çocuklar içeride.
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- source_sentence: Ağaçlarla çevrili bulvar denize üç bloktan daha az uzanıyor.
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sentences:
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- Deniz üç sokak bile uzakta değil.
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- Çocuk başını duvardaki bir delikten geçiriyor.
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- Denize ulaşmak için caddeden iki mil yol almanız gerekiyor.
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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: 0.7422539489671932
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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: 0.7503404448479346
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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-v3")
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# Run inference
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sentences = [
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'Ağaçlarla çevrili bulvar denize üç bloktan daha az uzanıyor.',
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'Deniz üç sokak bile uzakta değil.',
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'Denize ulaşmak için caddeden iki mil yol almanız gerekiyor.',
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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** | **0.7423** | **0.7503** |
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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`: 10
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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`: 10
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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 | 3.3654 | 2.9084 | 0.6624 | - |
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| 0.2309 | 200 | 2.6321 | 1.7277 | 0.7395 | - |
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| 0.3464 | 300 | 1.9629 | 1.5000 | 0.7512 | - |
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| 0.4619 | 400 | 1.6662 | 1.4965 | 0.7494 | - |
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| 0.5774 | 500 | 1.4712 | 1.5374 | 0.7418 | - |
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| 0.6928 | 600 | 1.0429 | 1.6301 | 0.7360 | - |
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| 0.8083 | 700 | 0.8995 | 2.1626 | 0.7044 | - |
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| 0.9238 | 800 | 0.7269 | 2.0440 | 0.6996 | - |
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| 1.0381 | 900 | 1.0584 | 1.6714 | 0.7438 | - |
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| 1.1536 | 1000 | 1.1864 | 1.5326 | 0.7495 | - |
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| 1.2691 | 1100 | 1.0193 | 1.4498 | 0.7518 | - |
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| 1.3845 | 1200 | 0.8237 | 1.5399 | 0.7506 | - |
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| 1.5 | 1300 | 0.8279 | 1.6747 | 0.7521 | - |
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| 1.6155 | 1400 | 0.626 | 1.5776 | 0.7453 | - |
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| 1.7309 | 1500 | 0.5396 | 1.8877 | 0.7139 | - |
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| 1.8464 | 1600 | 0.4294 | 2.2258 | 0.6947 | - |
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| 1.9619 | 1700 | 0.4988 | 1.8753 | 0.7204 | - |
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| 2.0762 | 1800 | 0.6987 | 1.5408 | 0.7524 | - |
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| 2.1917 | 1900 | 0.6684 | 1.4434 | 0.7618 | - |
|
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| 2.3072 | 2000 | 0.6072 | 1.4840 | 0.7520 | - |
|
|
| 2.4226 | 2100 | 0.5081 | 1.5225 | 0.7561 | - |
|
|
| 2.5381 | 2200 | 0.5216 | 1.5280 | 0.7514 | - |
|
|
| 2.6536 | 2300 | 0.2627 | 1.8830 | 0.7227 | - |
|
|
| 2.7691 | 2400 | 0.2585 | 1.9529 | 0.7221 | - |
|
|
| 2.8845 | 2500 | 0.129 | 2.2323 | 0.7047 | - |
|
|
| 3.0 | 2600 | 0.1698 | 2.2904 | 0.7063 | - |
|
|
| 3.1143 | 2700 | 0.5559 | 1.6110 | 0.7553 | - |
|
|
| 3.2298 | 2800 | 0.4356 | 1.5544 | 0.7508 | - |
|
|
| 3.3453 | 2900 | 0.3886 | 1.5437 | 0.7539 | - |
|
|
| 3.4607 | 3000 | 0.3573 | 1.6262 | 0.7539 | - |
|
|
| 3.5762 | 3100 | 0.2652 | 1.8391 | 0.7321 | - |
|
|
| 3.6917 | 3200 | 0.0765 | 2.0359 | 0.7186 | - |
|
|
| 3.8072 | 3300 | 0.0871 | 2.0946 | 0.7262 | - |
|
|
| 3.9226 | 3400 | 0.0586 | 2.2168 | 0.7093 | - |
|
|
| 4.0370 | 3500 | 0.1755 | 1.7567 | 0.7462 | - |
|
|
| 4.1524 | 3600 | 0.3397 | 1.7735 | 0.7442 | - |
|
|
| 4.2679 | 3700 | 0.3067 | 1.7475 | 0.7497 | - |
|
|
| 4.3834 | 3800 | 0.246 | 1.7075 | 0.7476 | - |
|
|
| 4.4988 | 3900 | 0.253 | 1.7648 | 0.7483 | - |
|
|
| 4.6143 | 4000 | 0.1223 | 1.9139 | 0.7246 | - |
|
|
| 4.7298 | 4100 | 0.0453 | 2.1138 | 0.7152 | - |
|
|
| 4.8453 | 4200 | 0.0241 | 2.2354 | 0.7240 | - |
|
|
| 4.9607 | 4300 | 0.0363 | 2.3080 | 0.7251 | - |
|
|
| 5.0751 | 4400 | 0.1897 | 1.7394 | 0.7494 | - |
|
|
| 5.1905 | 4500 | 0.2114 | 1.6929 | 0.7524 | - |
|
|
| 5.3060 | 4600 | 0.2101 | 1.7402 | 0.7556 | - |
|
|
| 5.4215 | 4700 | 0.1471 | 1.7990 | 0.7445 | - |
|
|
| 5.5370 | 4800 | 0.1783 | 1.8060 | 0.7456 | - |
|
|
| 5.6524 | 4900 | 0.0215 | 2.0118 | 0.7325 | - |
|
|
| 5.7679 | 5000 | 0.0083 | 2.0766 | 0.7265 | - |
|
|
| 5.8834 | 5100 | 0.0138 | 2.2054 | 0.7201 | - |
|
|
| 5.9988 | 5200 | 0.0144 | 2.1667 | 0.7164 | - |
|
|
| 6.1132 | 5300 | 0.2023 | 1.7309 | 0.7543 | - |
|
|
| 6.2286 | 5400 | 0.1356 | 1.6685 | 0.7622 | - |
|
|
| 6.3441 | 5500 | 0.1307 | 1.7292 | 0.7527 | - |
|
|
| 6.4596 | 5600 | 0.1222 | 1.8403 | 0.7435 | - |
|
|
| 6.5751 | 5700 | 0.1049 | 1.8456 | 0.7394 | - |
|
|
| 6.6905 | 5800 | 0.0051 | 1.9898 | 0.7362 | - |
|
|
| 6.8060 | 5900 | 0.0131 | 2.0532 | 0.7310 | - |
|
|
| 6.9215 | 6000 | 0.0132 | 2.2237 | 0.7186 | - |
|
|
| 7.0358 | 6100 | 0.0453 | 1.8965 | 0.7397 | - |
|
|
| 7.1513 | 6200 | 0.1109 | 1.7195 | 0.7550 | - |
|
|
| 7.2667 | 6300 | 0.1002 | 1.7547 | 0.7530 | - |
|
|
| 7.3822 | 6400 | 0.0768 | 1.7701 | 0.7433 | - |
|
|
| 7.4977 | 6500 | 0.0907 | 1.8472 | 0.7406 | - |
|
|
| 7.6132 | 6600 | 0.038 | 1.9162 | 0.7377 | - |
|
|
| 7.7286 | 6700 | 0.0151 | 1.9407 | 0.7312 | - |
|
|
| 7.8441 | 6800 | 0.0087 | 1.9657 | 0.7289 | - |
|
|
| 7.9596 | 6900 | 0.0104 | 2.0302 | 0.7227 | - |
|
|
| 8.0739 | 7000 | 0.0727 | 1.8692 | 0.7514 | - |
|
|
| 8.1894 | 7100 | 0.0733 | 1.8039 | 0.7520 | - |
|
|
| 8.3048 | 7200 | 0.0728 | 1.7400 | 0.7539 | - |
|
|
| 8.4203 | 7300 | 0.0537 | 1.8062 | 0.7461 | - |
|
|
| 8.5358 | 7400 | 0.059 | 1.8469 | 0.7489 | - |
|
|
| 8.6513 | 7500 | 0.0089 | 1.9033 | 0.7403 | - |
|
|
| 8.7667 | 7600 | 0.0034 | 1.9683 | 0.7354 | - |
|
|
| 8.8822 | 7700 | 0.0018 | 2.0075 | 0.7366 | - |
|
|
| 8.9977 | 7800 | 0.0023 | 2.0646 | 0.7322 | - |
|
|
| 9.1120 | 7900 | 0.0642 | 1.9063 | 0.7430 | - |
|
|
| 9.2275 | 8000 | 0.0596 | 1.8492 | 0.7468 | - |
|
|
| 9.3430 | 8100 | 0.0479 | 1.8180 | 0.7517 | - |
|
|
| 9.4584 | 8200 | 0.0561 | 1.8122 | 0.7468 | - |
|
|
| 9.5739 | 8300 | 0.0311 | 1.8528 | 0.7456 | - |
|
|
| 9.6894 | 8400 | 0.0069 | 1.8778 | 0.7447 | - |
|
|
| 9.8048 | 8500 | 0.0027 | 1.8989 | 0.7423 | - |
|
|
| 9.9203 | 8600 | 0.0093 | 1.9089 | 0.7423 | - |
|
|
| 9.9896 | 8660 | - | - | - | 0.7503 |
|
|
|
|
|
|
### Framework Versions
|
|
- Python: 3.10.14
|
|
- Sentence Transformers: 3.3.1
|
|
- Transformers: 4.46.3
|
|
- PyTorch: 2.3.0
|
|
- Accelerate: 1.1.1
|
|
- Datasets: 3.1.0
|
|
- Tokenizers: 0.20.3
|
|
|
|
## Citation
|
|
|
|
### BibTeX
|
|
|
|
#### 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",
|
|
}
|
|
```
|
|
|
|
#### MultipleNegativesRankingLoss
|
|
```bibtex
|
|
@misc{henderson2017efficient,
|
|
title={Efficient Natural Language Response Suggestion for Smart Reply},
|
|
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},
|
|
year={2017},
|
|
eprint={1705.00652},
|
|
archivePrefix={arXiv},
|
|
primaryClass={cs.CL}
|
|
}
|
|
```
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