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--- |
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base_model: BAAI/bge-m3 |
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datasets: [] |
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language: [] |
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library_name: sentence-transformers |
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metrics: |
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- pearson_cosine |
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- spearman_cosine |
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- pearson_manhattan |
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- spearman_manhattan |
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- pearson_euclidean |
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- spearman_euclidean |
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- pearson_dot |
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- spearman_dot |
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- pearson_max |
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- spearman_max |
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pipeline_tag: sentence-similarity |
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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:4532 |
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- loss:CoSENTLoss |
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widget: |
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- source_sentence: портативный проектор umiio a 008 |
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sentences: |
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- портативный проектор philips a 008 |
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- logitech c270i iptv |
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- детский электромобиль sundays land rover jj012 |
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- source_sentence: запчасти для швейных машин bernette |
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sentences: |
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- мфу samsung m428fdw |
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- запасные части для швейной машины bernette |
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- steelseries apex pro mini wireless |
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- source_sentence: сушильная машина maunfeld mfdm1410wh06 |
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sentences: |
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- кухонные уголки |
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- сушильная машина simens mfdm1410wh06 |
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- сетевой удлинитель евро eu-4 multi-protection 4usb qy-923 2500w |
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- source_sentence: монитор acer k242hql |
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sentences: |
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- multiflashlight armytek zippy green |
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- роутер mi router 4c r4cm dvb4231gl |
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- монитор acer k224hql |
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- source_sentence: набор моя первая кухня |
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sentences: |
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- кухонные наборы |
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- ea sports fc 23 ps4 |
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- da vinci белая |
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model-index: |
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- name: SentenceTransformer based on BAAI/bge-m3 |
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results: |
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- task: |
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type: semantic-similarity |
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name: Semantic Similarity |
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dataset: |
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name: sts dev |
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type: sts-dev |
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metrics: |
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- type: pearson_cosine |
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value: 0.9701810342203735 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.9168792089469636 |
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name: Spearman Cosine |
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- type: pearson_manhattan |
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value: 0.9695654298959763 |
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name: Pearson Manhattan |
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- type: spearman_manhattan |
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value: 0.9165761310923896 |
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name: Spearman Manhattan |
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- type: pearson_euclidean |
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value: 0.9696385323216731 |
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name: Pearson Euclidean |
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- type: spearman_euclidean |
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value: 0.9166348972420479 |
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name: Spearman Euclidean |
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- type: pearson_dot |
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value: 0.9631206697635591 |
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name: Pearson Dot |
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- type: spearman_dot |
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value: 0.9173046326579305 |
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name: Spearman Dot |
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- type: pearson_max |
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value: 0.9701810342203735 |
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name: Pearson Max |
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- type: spearman_max |
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value: 0.9173046326579305 |
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name: Spearman Max |
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--- |
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# SentenceTransformer based on BAAI/bge-m3 |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3). It maps sentences & paragraphs to a 1024-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:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) <!-- at revision babcf60cae0a1f438d7ade582983d4ba462303c2 --> |
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- **Maximum Sequence Length:** 8192 tokens |
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- **Output Dimensionality:** 1024 tokens |
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- **Similarity Function:** Cosine Similarity |
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<!-- - **Training Dataset:** Unknown --> |
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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': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel |
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(1): Pooling({'word_embedding_dimension': 1024, '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("seregadgl101/test_bge_2_10ep") |
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# Run inference |
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sentences = [ |
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'набор моя первая кухня', |
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'кухонные наборы', |
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'ea sports fc 23 ps4', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 1024] |
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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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#### Semantic Similarity |
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* Dataset: `sts-dev` |
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* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
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| Metric | Value | |
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|:--------------------|:-----------| |
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| pearson_cosine | 0.9702 | |
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| **spearman_cosine** | **0.9169** | |
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| pearson_manhattan | 0.9696 | |
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| spearman_manhattan | 0.9166 | |
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| pearson_euclidean | 0.9696 | |
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| spearman_euclidean | 0.9166 | |
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| pearson_dot | 0.9631 | |
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| spearman_dot | 0.9173 | |
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| pearson_max | 0.9702 | |
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| spearman_max | 0.9173 | |
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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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#### Unnamed Dataset |
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* Size: 4,532 training samples |
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* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence1 | sentence2 | score | |
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|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------| |
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| type | string | string | float | |
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| details | <ul><li>min: 4 tokens</li><li>mean: 14.45 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 13.09 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.6</li><li>max: 1.0</li></ul> | |
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* Samples: |
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| sentence1 | sentence2 | score | |
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|:-------------------------------------------------------------|:-------------------------------------------------------------|:-----------------| |
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| <code>батут evo jump internal 12ft</code> | <code>батут evo jump internal 12ft</code> | <code>1.0</code> | |
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| <code>наручные часы orient casual</code> | <code>наручные часы orient</code> | <code>1.0</code> | |
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| <code>электрический духовой шкаф weissgauff eov 19 mw</code> | <code>электрический духовой шкаф weissgauff eov 19 mx</code> | <code>0.4</code> | |
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* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) 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": "pairwise_cos_sim" |
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} |
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``` |
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### Evaluation Dataset |
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#### Unnamed Dataset |
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* Size: 504 evaluation samples |
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* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence1 | sentence2 | score | |
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|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------| |
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| type | string | string | float | |
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| details | <ul><li>min: 4 tokens</li><li>mean: 14.93 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 13.1 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.59</li><li>max: 1.0</li></ul> | |
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* Samples: |
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| sentence1 | sentence2 | score | |
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|:------------------------------------------------------------------------------|:--------------------------------------------------------|:-----------------| |
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| <code>потолочный светильник yeelight smart led ceiling light c2001s500</code> | <code>yeelight smart led ceiling light c2001s500</code> | <code>1.0</code> | |
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| <code>канцелярские принадлежности</code> | <code>канцелярские принадлежности разные</code> | <code>0.4</code> | |
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| <code>usb-магнитола acv avs-1718g</code> | <code>автомагнитола acv avs-1718g</code> | <code>1.0</code> | |
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* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) 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": "pairwise_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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- `learning_rate`: 2e-05 |
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- `num_train_epochs`: 10 |
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- `warmup_ratio`: 0.1 |
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- `save_only_model`: True |
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- `seed`: 33 |
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- `fp16`: True |
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- `load_best_model_at_end`: 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`: 8 |
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- `per_device_eval_batch_size`: 8 |
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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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- `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`: True |
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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`: 33 |
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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`: True |
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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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- `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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- `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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<details><summary>Click to expand</summary> |
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| Epoch | Step | Training Loss | loss | sts-dev_spearman_cosine | |
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|:----------:|:--------:|:-------------:|:----------:|:-----------------------:| |
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| 0.0882 | 50 | - | 2.7444 | 0.4991 | |
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| 0.1764 | 100 | - | 2.5535 | 0.6093 | |
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| 0.2646 | 150 | - | 2.3365 | 0.6761 | |
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| 0.3527 | 200 | - | 2.1920 | 0.7247 | |
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| 0.4409 | 250 | - | 2.2210 | 0.7446 | |
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| 0.5291 | 300 | - | 2.1432 | 0.7610 | |
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| 0.6173 | 350 | - | 2.2488 | 0.7769 | |
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| 0.7055 | 400 | - | 2.3736 | 0.7749 | |
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| 0.7937 | 450 | - | 2.0688 | 0.7946 | |
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| 0.8818 | 500 | 2.3647 | 2.5331 | 0.7879 | |
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| 0.9700 | 550 | - | 2.1087 | 0.7742 | |
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| 1.0582 | 600 | - | 2.1302 | 0.8068 | |
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| 1.1464 | 650 | - | 2.2669 | 0.8114 | |
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| 1.2346 | 700 | - | 2.0269 | 0.8039 | |
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| 1.3228 | 750 | - | 2.2095 | 0.8138 | |
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| 1.4109 | 800 | - | 2.5288 | 0.8190 | |
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| 1.4991 | 850 | - | 2.3442 | 0.8222 | |
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| 1.5873 | 900 | - | 2.3759 | 0.8289 | |
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| 1.6755 | 950 | - | 2.1893 | 0.8280 | |
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| 1.7637 | 1000 | 2.0682 | 2.0056 | 0.8426 | |
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| 1.8519 | 1050 | - | 2.0832 | 0.8527 | |
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|
| 1.9400 | 1100 | - | 2.0336 | 0.8515 | |
|
|
| 2.0282 | 1150 | - | 2.0571 | 0.8591 | |
|
|
| 2.1164 | 1200 | - | 2.1516 | 0.8565 | |
|
|
| 2.2046 | 1250 | - | 2.2035 | 0.8602 | |
|
|
| 2.2928 | 1300 | - | 2.5294 | 0.8513 | |
|
|
| 2.3810 | 1350 | - | 2.4177 | 0.8647 | |
|
|
| 2.4691 | 1400 | - | 2.1630 | 0.8709 | |
|
|
| 2.5573 | 1450 | - | 2.1279 | 0.8661 | |
|
|
| 2.6455 | 1500 | 1.678 | 2.1639 | 0.8744 | |
|
|
| 2.7337 | 1550 | - | 2.2592 | 0.8799 | |
|
|
| 2.8219 | 1600 | - | 2.2288 | 0.8822 | |
|
|
| 2.9101 | 1650 | - | 2.2427 | 0.8831 | |
|
|
| 2.9982 | 1700 | - | 2.4380 | 0.8776 | |
|
|
| 3.0864 | 1750 | - | 2.1689 | 0.8826 | |
|
|
| 3.1746 | 1800 | - | 1.8099 | 0.8868 | |
|
|
| 3.2628 | 1850 | - | 2.0881 | 0.8832 | |
|
|
| 3.3510 | 1900 | - | 2.0785 | 0.8892 | |
|
|
| 3.4392 | 1950 | - | 2.2512 | 0.8865 | |
|
|
| 3.5273 | 2000 | 1.2168 | 2.1249 | 0.8927 | |
|
|
| 3.6155 | 2050 | - | 2.1179 | 0.8950 | |
|
|
| 3.7037 | 2100 | - | 2.1932 | 0.8973 | |
|
|
| 3.7919 | 2150 | - | 2.2628 | 0.8967 | |
|
|
| 3.8801 | 2200 | - | 2.0764 | 0.8972 | |
|
|
| 3.9683 | 2250 | - | 1.9575 | 0.9012 | |
|
|
| 4.0564 | 2300 | - | 2.3302 | 0.8985 | |
|
|
| 4.1446 | 2350 | - | 2.3008 | 0.8980 | |
|
|
| 4.2328 | 2400 | - | 2.2886 | 0.8968 | |
|
|
| 4.3210 | 2450 | - | 2.1694 | 0.8973 | |
|
|
| 4.4092 | 2500 | 1.0851 | 2.1102 | 0.9010 | |
|
|
| 4.4974 | 2550 | - | 2.2596 | 0.9021 | |
|
|
| 4.5855 | 2600 | - | 2.1944 | 0.9019 | |
|
|
| 4.6737 | 2650 | - | 2.0728 | 0.9029 | |
|
|
| 4.7619 | 2700 | - | 2.4573 | 0.9031 | |
|
|
| 4.8501 | 2750 | - | 2.2306 | 0.9057 | |
|
|
| 4.9383 | 2800 | - | 2.2637 | 0.9068 | |
|
|
| 5.0265 | 2850 | - | 2.5110 | 0.9068 | |
|
|
| 5.1146 | 2900 | - | 2.6613 | 0.9042 | |
|
|
| 5.2028 | 2950 | - | 2.4713 | 0.9070 | |
|
|
| 5.2910 | 3000 | 0.8143 | 2.3709 | 0.9082 | |
|
|
| 5.3792 | 3050 | - | 2.6083 | 0.9058 | |
|
|
| 5.4674 | 3100 | - | 2.5377 | 0.9044 | |
|
|
| 5.5556 | 3150 | - | 2.3146 | 0.9071 | |
|
|
| 5.6437 | 3200 | - | 2.2603 | 0.9085 | |
|
|
| 5.7319 | 3250 | - | 2.5842 | 0.9068 | |
|
|
| 5.8201 | 3300 | - | 2.6045 | 0.9093 | |
|
|
| 5.9083 | 3350 | - | 2.6207 | 0.9103 | |
|
|
| 5.9965 | 3400 | - | 2.5992 | 0.9098 | |
|
|
| 6.0847 | 3450 | - | 2.7799 | 0.9090 | |
|
|
| 6.1728 | 3500 | 0.5704 | 2.7198 | 0.9098 | |
|
|
| 6.2610 | 3550 | - | 2.9783 | 0.9089 | |
|
|
| 6.3492 | 3600 | - | 2.4165 | 0.9120 | |
|
|
| 6.4374 | 3650 | - | 2.4488 | 0.9122 | |
|
|
| 6.5256 | 3700 | - | 2.6764 | 0.9113 | |
|
|
| 6.6138 | 3750 | - | 2.5327 | 0.9130 | |
|
|
| 6.7019 | 3800 | - | 2.5875 | 0.9129 | |
|
|
| 6.7901 | 3850 | - | 2.7036 | 0.9130 | |
|
|
| 6.8783 | 3900 | - | 2.7566 | 0.9120 | |
|
|
| 6.9665 | 3950 | - | 2.5488 | 0.9127 | |
|
|
| 7.0547 | 4000 | 0.4287 | 2.8512 | 0.9127 | |
|
|
| 7.1429 | 4050 | - | 2.7361 | 0.9128 | |
|
|
| 7.2310 | 4100 | - | 2.7434 | 0.9135 | |
|
|
| 7.3192 | 4150 | - | 2.9410 | 0.9129 | |
|
|
| 7.4074 | 4200 | - | 2.9452 | 0.9126 | |
|
|
| 7.4956 | 4250 | - | 2.8665 | 0.9140 | |
|
|
| 7.5838 | 4300 | - | 2.8215 | 0.9145 | |
|
|
| 7.6720 | 4350 | - | 2.6978 | 0.9147 | |
|
|
| 7.7601 | 4400 | - | 2.8445 | 0.9143 | |
|
|
| 7.8483 | 4450 | - | 2.6041 | 0.9155 | |
|
|
| 7.9365 | 4500 | 0.3099 | 2.7219 | 0.9155 | |
|
|
| 8.0247 | 4550 | - | 2.7180 | 0.9160 | |
|
|
| 8.1129 | 4600 | - | 2.6906 | 0.9160 | |
|
|
| 8.2011 | 4650 | - | 2.8628 | 0.9156 | |
|
|
| 8.2892 | 4700 | - | 2.7820 | 0.9158 | |
|
|
| 8.3774 | 4750 | - | 2.8457 | 0.9157 | |
|
|
| 8.4656 | 4800 | - | 2.7286 | 0.9160 | |
|
|
| 8.5538 | 4850 | - | 2.7131 | 0.9164 | |
|
|
| 8.6420 | 4900 | - | 2.8368 | 0.9165 | |
|
|
| 8.7302 | 4950 | - | 2.8033 | 0.9167 | |
|
|
| 8.8183 | 5000 | 0.2342 | 2.7307 | 0.9169 | |
|
|
| 8.9065 | 5050 | - | 2.8483 | 0.9167 | |
|
|
| 8.9947 | 5100 | - | 2.9736 | 0.9167 | |
|
|
| 9.0829 | 5150 | - | 2.9151 | 0.9168 | |
|
|
| 9.1711 | 5200 | - | 2.9375 | 0.9167 | |
|
|
| 9.2593 | 5250 | - | 2.9968 | 0.9168 | |
|
|
| 9.3474 | 5300 | - | 3.0024 | 0.9167 | |
|
|
| 9.4356 | 5350 | - | 2.9444 | 0.9167 | |
|
|
| 9.5238 | 5400 | - | 2.9477 | 0.9167 | |
|
|
| 9.6120 | 5450 | - | 2.9205 | 0.9168 | |
|
|
| **9.7002** | **5500** | **0.1639** | **2.9286** | **0.9167** | |
|
|
| 9.7884 | 5550 | - | 2.9421 | 0.9168 | |
|
|
| 9.8765 | 5600 | - | 2.9733 | 0.9168 | |
|
|
| 9.9647 | 5650 | - | 2.9777 | 0.9169 | |
|
|
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
</details> |
|
|
|
|
|
### Framework Versions |
|
|
- Python: 3.10.12 |
|
|
- Sentence Transformers: 3.0.1 |
|
|
- Transformers: 4.41.2 |
|
|
- PyTorch: 2.1.2+cu121 |
|
|
- Accelerate: 0.31.0 |
|
|
- Datasets: 2.20.0 |
|
|
- Tokenizers: 0.19.1 |
|
|
|
|
|
## 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", |
|
|
} |
|
|
``` |
|
|
|
|
|
#### CoSENTLoss |
|
|
```bibtex |
|
|
@online{kexuefm-8847, |
|
|
title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT}, |
|
|
author={Su Jianlin}, |
|
|
year={2022}, |
|
|
month={Jan}, |
|
|
url={https://kexue.fm/archives/8847}, |
|
|
} |
|
|
``` |
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