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--- |
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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:111476 |
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- loss:CosineSimilarityLoss |
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base_model: sergeyzh/LaBSE-ru-sts |
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widget: |
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- source_sentence: 'трюковый самокат plank 180 белый ' |
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sentences: |
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- смарт-телевизор 75 sony kd-75x950h |
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- самокат для трюков плэнк 1.80 м белый |
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- xiaomi mi 11 8gb 128gb |
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- source_sentence: 'вейп vaporesso xros ' |
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sentences: |
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- садовая ограда классика 4 2 м белый |
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- кухонные весы |
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- электронная сигарета voopoo drag |
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- source_sentence: серьги l atelier precieux 1628710 |
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sentences: |
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- фильтр hepa для пылесоса варис st400 |
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- потолочная люстра майтон nostalgia ceiling chandelier mod048pl-06g |
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- серьги atelier de bijoux 1628712 |
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- source_sentence: 'мобильный геймпад триггерами x2 ' |
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sentences: |
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- электроскутер nitro pro milano 750w led |
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- наушники без проводов мейзу ep52 lite |
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- геймпад с функцией триггеров x2 |
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- source_sentence: комод 7 рисунком машинки 4 ящика |
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sentences: |
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- удлинитель far f 505 d lara выключателем 2 0м |
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- беззеркальный фотоаппарат nikon z50 kit 16-50mm ilce-7cl красный |
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- комод 8 с изображением супергероев 6 ящиков |
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datasets: |
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- seregadgl/data_cross_gpt_139k |
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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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- cosine_accuracy_threshold |
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- cosine_f1 |
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- cosine_f1_threshold |
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- cosine_precision |
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- cosine_recall |
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- cosine_ap |
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- cosine_mcc |
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model-index: |
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- name: SentenceTransformer based on sergeyzh/LaBSE-ru-sts |
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results: |
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- task: |
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type: binary-classification |
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name: Binary Classification |
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dataset: |
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name: eval |
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type: eval |
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metrics: |
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- type: cosine_accuracy |
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value: 0.9722640832436311 |
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name: Cosine Accuracy |
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- type: cosine_accuracy_threshold |
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value: 0.630459189414978 |
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name: Cosine Accuracy Threshold |
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- type: cosine_f1 |
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value: 0.9724366041896361 |
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name: Cosine F1 |
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- type: cosine_f1_threshold |
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value: 0.5821653008460999 |
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name: Cosine F1 Threshold |
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- type: cosine_precision |
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value: 0.9647847565278758 |
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name: Cosine Precision |
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- type: cosine_recall |
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value: 0.9802107980210798 |
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name: Cosine Recall |
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- type: cosine_ap |
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value: 0.9945729266353226 |
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name: Cosine Ap |
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- type: cosine_mcc |
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value: 0.9445047865635516 |
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name: Cosine Mcc |
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--- |
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# SentenceTransformer based on sergeyzh/LaBSE-ru-sts |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sergeyzh/LaBSE-ru-sts](https://huggingface.co/sergeyzh/LaBSE-ru-sts) on the [data_cross_gpt_139k](https://huggingface.co/datasets/seregadgl/data_cross_gpt_139k) 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:** [sergeyzh/LaBSE-ru-sts](https://huggingface.co/sergeyzh/LaBSE-ru-sts) <!-- at revision 00c333ce29c9ad739f48baca9a578cd1e85094d4 --> |
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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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- [data_cross_gpt_139k](https://huggingface.co/datasets/seregadgl/data_cross_gpt_139k) |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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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: BertModel |
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(1): Pooling({'word_embedding_dimension': 768, '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}) |
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(2): Normalize() |
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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("seregadgl/sts_v11") |
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# Run inference |
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sentences = [ |
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'комод 7 рисунком машинки 4 ящика', |
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'комод 8 с изображением супергероев 6 ящиков', |
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'беззеркальный фотоаппарат nikon z50 kit 16-50mm ilce-7cl красный', |
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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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#### Binary Classification |
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* Dataset: `eval` |
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* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) |
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| Metric | Value | |
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|:--------------------------|:-----------| |
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| cosine_accuracy | 0.9723 | |
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| cosine_accuracy_threshold | 0.6305 | |
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| cosine_f1 | 0.9724 | |
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| cosine_f1_threshold | 0.5822 | |
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| cosine_precision | 0.9648 | |
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| cosine_recall | 0.9802 | |
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| **cosine_ap** | **0.9946** | |
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| cosine_mcc | 0.9445 | |
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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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#### data_cross_gpt_139k |
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* Dataset: [data_cross_gpt_139k](https://huggingface.co/datasets/seregadgl/data_cross_gpt_139k) at [9e1f5ca](https://huggingface.co/datasets/seregadgl/data_cross_gpt_139k/tree/9e1f5ca30088e6f61ca5b9a742b38ef2c4fc7f3e) |
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* Size: 111,476 training samples |
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* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence1 | sentence2 | label | |
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|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| |
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| type | string | string | float | |
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| details | <ul><li>min: 3 tokens</li><li>mean: 14.84 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 15.64 tokens</li><li>max: 55 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.47</li><li>max: 1.0</li></ul> | |
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* Samples: |
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| sentence1 | sentence2 | label | |
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|:-------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------|:-----------------| |
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| <code>нож кухонный 21см синий</code> | <code>кухонный нож 22см зелёный</code> | <code>0.0</code> | |
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| <code>блок питания универсальный для мерцающих флэш гирлянд rich led бахрома занавес нить белый</code> | <code>адаптер питания для мигающих led гирлянд "luminous decor" бахрома занавес нить зелёный</code> | <code>0.0</code> | |
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| <code>защитная пленка для apple iphone 6 прозрачная </code> | <code>protective film for apple iphone 6 transparent</code> | <code>1.0</code> | |
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* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: |
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```json |
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{ |
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"loss_fct": "torch.nn.modules.loss.MSELoss" |
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} |
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``` |
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### Evaluation Dataset |
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#### data_cross_gpt_139k |
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* Dataset: [data_cross_gpt_139k](https://huggingface.co/datasets/seregadgl/data_cross_gpt_139k) at [9e1f5ca](https://huggingface.co/datasets/seregadgl/data_cross_gpt_139k/tree/9e1f5ca30088e6f61ca5b9a742b38ef2c4fc7f3e) |
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* Size: 27,870 evaluation samples |
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* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence1 | sentence2 | label | |
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|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| |
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| type | string | string | float | |
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| details | <ul><li>min: 3 tokens</li><li>mean: 15.05 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 15.57 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.48</li><li>max: 1.0</li></ul> | |
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* Samples: |
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| sentence1 | sentence2 | label | |
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|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------|:-----------------| |
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| <code>сумка дорожная складная полет оранжевая bradex td 0599 </code> | <code>сумка для путешествий складная брадекс orange</code> | <code>1.0</code> | |
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| <code>наушники sennheiser hd 450bt белый </code> | <code>наушники сенхайзер hd 450bt white</code> | <code>1.0</code> | |
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| <code>перчатки stg al-05-1871 синие серые черные зеленыеполноразмерные xl</code> | <code>перчатки stg al-05-1871 blue gray black green full size xl</code> | <code>1.0</code> | |
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* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: |
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```json |
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{ |
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"loss_fct": "torch.nn.modules.loss.MSELoss" |
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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`: 32 |
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- `per_device_eval_batch_size`: 32 |
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- `learning_rate`: 4.7459131195420915e-05 |
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- `weight_decay`: 0.03196240090522689 |
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- `num_train_epochs`: 2 |
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- `warmup_ratio`: 0.014344463935915175 |
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- `fp16`: True |
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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`: 32 |
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- `per_device_eval_batch_size`: 32 |
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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`: 4.7459131195420915e-05 |
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- `weight_decay`: 0.03196240090522689 |
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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`: 2 |
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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`: 0.014344463935915175 |
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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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- `tp_size`: 0 |
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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`: None |
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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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- `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`: batch_sampler |
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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 | eval_cosine_ap | |
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|:------:|:----:|:-------------:|:---------------:|:--------------:| |
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| 0.0287 | 100 | 0.189 | - | - | |
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| 0.0574 | 200 | 0.0695 | - | - | |
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| 0.0861 | 300 | 0.067 | - | - | |
|
|
| 0.1148 | 400 | 0.0643 | - | - | |
|
|
| 0.1435 | 500 | 0.0594 | 0.0549 | 0.9862 | |
|
|
| 0.1722 | 600 | 0.0565 | - | - | |
|
|
| 0.2009 | 700 | 0.0535 | - | - | |
|
|
| 0.2296 | 800 | 0.0506 | - | - | |
|
|
| 0.2583 | 900 | 0.0549 | - | - | |
|
|
| 0.2870 | 1000 | 0.0535 | 0.0451 | 0.9888 | |
|
|
| 0.3157 | 1100 | 0.0492 | - | - | |
|
|
| 0.3444 | 1200 | 0.0499 | - | - | |
|
|
| 0.3731 | 1300 | 0.0486 | - | - | |
|
|
| 0.4018 | 1400 | 0.0458 | - | - | |
|
|
| 0.4305 | 1500 | 0.0458 | 0.0419 | 0.9877 | |
|
|
| 0.4592 | 1600 | 0.0502 | - | - | |
|
|
| 0.4879 | 1700 | 0.045 | - | - | |
|
|
| 0.5166 | 1800 | 0.0435 | - | - | |
|
|
| 0.5454 | 1900 | 0.0426 | - | - | |
|
|
| 0.5741 | 2000 | 0.0422 | 0.0386 | 0.9906 | |
|
|
| 0.6028 | 2100 | 0.0436 | - | - | |
|
|
| 0.6315 | 2200 | 0.043 | - | - | |
|
|
| 0.6602 | 2300 | 0.0432 | - | - | |
|
|
| 0.6889 | 2400 | 0.0397 | - | - | |
|
|
| 0.7176 | 2500 | 0.0394 | 0.0357 | 0.9903 | |
|
|
| 0.7463 | 2600 | 0.039 | - | - | |
|
|
| 0.7750 | 2700 | 0.0398 | - | - | |
|
|
| 0.8037 | 2800 | 0.0394 | - | - | |
|
|
| 0.8324 | 2900 | 0.0426 | - | - | |
|
|
| 0.8611 | 3000 | 0.0345 | 0.0341 | 0.9921 | |
|
|
| 0.8898 | 3100 | 0.0361 | - | - | |
|
|
| 0.9185 | 3200 | 0.0365 | - | - | |
|
|
| 0.9472 | 3300 | 0.0401 | - | - | |
|
|
| 0.9759 | 3400 | 0.0391 | - | - | |
|
|
| 1.0046 | 3500 | 0.0342 | 0.0310 | 0.9928 | |
|
|
| 1.0333 | 3600 | 0.0267 | - | - | |
|
|
| 1.0620 | 3700 | 0.0264 | - | - | |
|
|
| 1.0907 | 3800 | 0.0263 | - | - | |
|
|
| 1.1194 | 3900 | 0.0248 | - | - | |
|
|
| 1.1481 | 4000 | 0.0282 | 0.0301 | 0.9928 | |
|
|
| 1.1768 | 4100 | 0.0279 | - | - | |
|
|
| 1.2055 | 4200 | 0.0258 | - | - | |
|
|
| 1.2342 | 4300 | 0.0248 | - | - | |
|
|
| 1.2629 | 4400 | 0.0289 | - | - | |
|
|
| 1.2916 | 4500 | 0.0261 | 0.0291 | 0.9935 | |
|
|
| 1.3203 | 4600 | 0.0262 | - | - | |
|
|
| 1.3490 | 4700 | 0.0276 | - | - | |
|
|
| 1.3777 | 4800 | 0.0256 | - | - | |
|
|
| 1.4064 | 4900 | 0.0272 | - | - | |
|
|
| 1.4351 | 5000 | 0.0283 | 0.0284 | 0.9939 | |
|
|
| 1.4638 | 5100 | 0.0254 | - | - | |
|
|
| 1.4925 | 5200 | 0.0252 | - | - | |
|
|
| 1.5212 | 5300 | 0.0234 | - | - | |
|
|
| 1.5499 | 5400 | 0.0228 | - | - | |
|
|
| 1.5786 | 5500 | 0.0248 | 0.0277 | 0.9941 | |
|
|
| 1.6073 | 5600 | 0.024 | - | - | |
|
|
| 1.6361 | 5700 | 0.0225 | - | - | |
|
|
| 1.6648 | 5800 | 0.0234 | - | - | |
|
|
| 1.6935 | 5900 | 0.0226 | - | - | |
|
|
| 1.7222 | 6000 | 0.0248 | 0.0265 | 0.9942 | |
|
|
| 1.7509 | 6100 | 0.0247 | - | - | |
|
|
| 1.7796 | 6200 | 0.0219 | - | - | |
|
|
| 1.8083 | 6300 | 0.026 | - | - | |
|
|
| 1.8370 | 6400 | 0.0209 | - | - | |
|
|
| 1.8657 | 6500 | 0.0252 | 0.0262 | 0.9945 | |
|
|
| 1.8944 | 6600 | 0.0218 | - | - | |
|
|
| 1.9231 | 6700 | 0.0223 | - | - | |
|
|
| 1.9518 | 6800 | 0.0228 | - | - | |
|
|
| 1.9805 | 6900 | 0.0242 | - | - | |
|
|
| 2.0 | 6968 | - | 0.0257 | 0.9946 | |
|
|
|
|
|
|
|
|
### Framework Versions |
|
|
- Python: 3.11.11 |
|
|
- Sentence Transformers: 4.1.0 |
|
|
- Transformers: 4.51.3 |
|
|
- PyTorch: 2.6.0+cu124 |
|
|
- Accelerate: 1.5.2 |
|
|
- Datasets: 3.6.0 |
|
|
- Tokenizers: 0.21.1 |
|
|
|
|
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## Citation |
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|
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### BibTeX |
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|
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#### Sentence Transformers |
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|
```bibtex |
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|
@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", |
|
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url = "https://arxiv.org/abs/1908.10084", |
|
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} |
|
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``` |
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