| ---
|
| language:
|
| - en
|
| tags:
|
| - sentence-transformers
|
| - sentence-similarity
|
| - feature-extraction
|
| - generated_from_trainer
|
| - dataset_size:404290
|
| - loss:OnlineContrastiveLoss
|
| base_model: sentence-transformers/stsb-distilbert-base
|
| widget:
|
| - source_sentence: Why Modi is putting a ban on 500 and 1000 notes?
|
| sentences:
|
| - Why making multiple fake accounts on Quora is illegal?
|
| - What are the advantages of the decision taken by the Government of India to scrap
|
| out 500 and 1000 rupees notes?
|
| - Why should I go for internships?
|
| - source_sentence: Where can I buy cheap t-shirts?
|
| sentences:
|
| - Where can I buy cheap wholesale t-shirts?
|
| - How can I make money from a blog?
|
| - What are the best places to shop in Charleston, SC?
|
| - source_sentence: What are the most important mobile applications?
|
| sentences:
|
| - How can I tell if my wife's vagina had a bigger penis inside?
|
| - What is the most important apps in your phone?
|
| - What do you think Ned Stark would have done or said to Jon Snow if he was able
|
| to join the Night’s Watch or escaped his beheading?
|
| - source_sentence: What is the whole process for making Android games with high graphics?
|
| sentences:
|
| - What lf I don't accept Jesus as God?
|
| - I have to masturbate3 times to feel an orgasm sometimes only2 times what is wrong
|
| with me I went to the doctor and they do not believe meWhat's wrong?
|
| - What does a healthy diet consist of?
|
| - source_sentence: Why do so many religious people believe in healing miracles?
|
| sentences:
|
| - Is Warframe better than Destiny?
|
| - What do you like about China?
|
| - Is believing in God a bad thing?
|
| datasets:
|
| - sentence-transformers/quora-duplicates
|
| pipeline_tag: sentence-similarity
|
| library_name: sentence-transformers
|
| metrics:
|
| - cosine_accuracy
|
| - cosine_accuracy_threshold
|
| - cosine_f1
|
| - cosine_f1_threshold
|
| - cosine_precision
|
| - cosine_recall
|
| - cosine_ap
|
| - cosine_mcc
|
| - average_precision
|
| - f1
|
| - precision
|
| - recall
|
| - threshold
|
| - cosine_accuracy@1
|
| - cosine_accuracy@3
|
| - cosine_accuracy@5
|
| - cosine_accuracy@10
|
| - cosine_precision@1
|
| - cosine_precision@3
|
| - cosine_precision@5
|
| - cosine_precision@10
|
| - cosine_recall@1
|
| - cosine_recall@3
|
| - cosine_recall@5
|
| - cosine_recall@10
|
| - cosine_ndcg@10
|
| - cosine_mrr@10
|
| - cosine_map@100
|
| model-index:
|
| - name: SentenceTransformer based on sentence-transformers/stsb-distilbert-base
|
| results:
|
| - task:
|
| type: binary-classification
|
| name: Binary Classification
|
| dataset:
|
| name: quora duplicates
|
| type: quora-duplicates
|
| metrics:
|
| - type: cosine_accuracy
|
| value: 0.877
|
| name: Cosine Accuracy
|
| - type: cosine_accuracy_threshold
|
| value: 0.7857047319412231
|
| name: Cosine Accuracy Threshold
|
| - type: cosine_f1
|
| value: 0.8516284680337757
|
| name: Cosine F1
|
| - type: cosine_f1_threshold
|
| value: 0.774639368057251
|
| name: Cosine F1 Threshold
|
| - type: cosine_precision
|
| value: 0.8209302325581396
|
| name: Cosine Precision
|
| - type: cosine_recall
|
| value: 0.8847117794486216
|
| name: Cosine Recall
|
| - type: cosine_ap
|
| value: 0.8988328505183655
|
| name: Cosine Ap
|
| - type: cosine_mcc
|
| value: 0.7483655051498526
|
| name: Cosine Mcc
|
| - task:
|
| type: paraphrase-mining
|
| name: Paraphrase Mining
|
| dataset:
|
| name: quora duplicates dev
|
| type: quora-duplicates-dev
|
| metrics:
|
| - type: average_precision
|
| value: 0.5483042026376685
|
| name: Average Precision
|
| - type: f1
|
| value: 0.5606415792720543
|
| name: F1
|
| - type: precision
|
| value: 0.5539301735907939
|
| name: Precision
|
| - type: recall
|
| value: 0.5675176100314733
|
| name: Recall
|
| - type: threshold
|
| value: 0.8631762564182281
|
| name: Threshold
|
| - task:
|
| type: information-retrieval
|
| name: Information Retrieval
|
| dataset:
|
| name: Unknown
|
| type: unknown
|
| metrics:
|
| - type: cosine_accuracy@1
|
| value: 0.9308
|
| name: Cosine Accuracy@1
|
| - type: cosine_accuracy@3
|
| value: 0.969
|
| name: Cosine Accuracy@3
|
| - type: cosine_accuracy@5
|
| value: 0.9778
|
| name: Cosine Accuracy@5
|
| - type: cosine_accuracy@10
|
| value: 0.9854
|
| name: Cosine Accuracy@10
|
| - type: cosine_precision@1
|
| value: 0.9308
|
| name: Cosine Precision@1
|
| - type: cosine_precision@3
|
| value: 0.4145333333333333
|
| name: Cosine Precision@3
|
| - type: cosine_precision@5
|
| value: 0.26696000000000003
|
| name: Cosine Precision@5
|
| - type: cosine_precision@10
|
| value: 0.14144
|
| name: Cosine Precision@10
|
| - type: cosine_recall@1
|
| value: 0.8008592901379665
|
| name: Cosine Recall@1
|
| - type: cosine_recall@3
|
| value: 0.9314231047351341
|
| name: Cosine Recall@3
|
| - type: cosine_recall@5
|
| value: 0.9558165998609235
|
| name: Cosine Recall@5
|
| - type: cosine_recall@10
|
| value: 0.9743579383296442
|
| name: Cosine Recall@10
|
| - type: cosine_ndcg@10
|
| value: 0.9511384841680516
|
| name: Cosine Ndcg@10
|
| - type: cosine_mrr@10
|
| value: 0.9511976190476192
|
| name: Cosine Mrr@10
|
| - type: cosine_map@100
|
| value: 0.939071878001028
|
| name: Cosine Map@100
|
| ---
|
|
|
| # SentenceTransformer based on sentence-transformers/stsb-distilbert-base
|
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/stsb-distilbert-base](https://huggingface.co/sentence-transformers/stsb-distilbert-base) on the [quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) 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.
|
|
|
| ## Model Details
|
|
|
| ### Model Description
|
| - **Model Type:** Sentence Transformer
|
| - **Base model:** [sentence-transformers/stsb-distilbert-base](https://huggingface.co/sentence-transformers/stsb-distilbert-base) <!-- at revision 8ea752b88e5f7239f96bdde0bc62e265c3999eec -->
|
| - **Maximum Sequence Length:** 128 tokens
|
| - **Output Dimensionality:** 768 dimensions
|
| - **Similarity Function:** Cosine Similarity
|
| - **Training Dataset:**
|
| - [quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates)
|
| - **Language:** en
|
| <!-- - **License:** Unknown -->
|
|
|
| ### Model Sources
|
|
|
| - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
|
| - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
|
| - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
|
|
|
| ### Full Model Architecture
|
|
|
| ```
|
| SentenceTransformer(
|
| (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel
|
| (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})
|
| )
|
| ```
|
|
|
| ## Usage
|
|
|
| ### Direct Usage (Sentence Transformers)
|
|
|
| First install the Sentence Transformers library:
|
|
|
| ```bash
|
| pip install -U sentence-transformers
|
| ```
|
|
|
| Then you can load this model and run inference.
|
| ```python
|
| from sentence_transformers import SentenceTransformer
|
|
|
| # Download from the 🤗 Hub
|
| model = SentenceTransformer("omega5505/stsb-distilbert-base-ocl")
|
| # Run inference
|
| sentences = [
|
| 'Why do so many religious people believe in healing miracles?',
|
| 'Is believing in God a bad thing?',
|
| 'What do you like about China?',
|
| ]
|
| embeddings = model.encode(sentences)
|
| print(embeddings.shape)
|
| # [3, 768]
|
|
|
| # Get the similarity scores for the embeddings
|
| similarities = model.similarity(embeddings, embeddings)
|
| print(similarities.shape)
|
| # [3, 3]
|
| ```
|
|
|
| <!--
|
| ### Direct Usage (Transformers)
|
|
|
| <details><summary>Click to see the direct usage in Transformers</summary>
|
|
|
| </details>
|
| -->
|
|
|
| <!--
|
| ### Downstream Usage (Sentence Transformers)
|
|
|
| You can finetune this model on your own dataset.
|
|
|
| <details><summary>Click to expand</summary>
|
|
|
| </details>
|
| -->
|
|
|
| <!--
|
| ### Out-of-Scope Use
|
|
|
| *List how the model may foreseeably be misused and address what users ought not to do with the model.*
|
| -->
|
|
|
| ## Evaluation
|
|
|
| ### Metrics
|
|
|
| #### Binary Classification
|
|
|
| * Dataset: `quora-duplicates`
|
| * Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
|
|
|
| | Metric | Value |
|
| |:--------------------------|:-----------|
|
| | cosine_accuracy | 0.877 |
|
| | cosine_accuracy_threshold | 0.7857 |
|
| | cosine_f1 | 0.8516 |
|
| | cosine_f1_threshold | 0.7746 |
|
| | cosine_precision | 0.8209 |
|
| | cosine_recall | 0.8847 |
|
| | **cosine_ap** | **0.8988** |
|
| | cosine_mcc | 0.7484 |
|
|
|
| #### Paraphrase Mining
|
|
|
| * Dataset: `quora-duplicates-dev`
|
| * Evaluated with [<code>ParaphraseMiningEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.ParaphraseMiningEvaluator)
|
|
|
| | Metric | Value |
|
| |:----------------------|:-----------|
|
| | **average_precision** | **0.5483** |
|
| | f1 | 0.5606 |
|
| | precision | 0.5539 |
|
| | recall | 0.5675 |
|
| | threshold | 0.8632 |
|
|
|
| #### Information Retrieval
|
|
|
| * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
|
|
|
| | Metric | Value |
|
| |:--------------------|:-----------|
|
| | cosine_accuracy@1 | 0.9308 |
|
| | cosine_accuracy@3 | 0.969 |
|
| | cosine_accuracy@5 | 0.9778 |
|
| | cosine_accuracy@10 | 0.9854 |
|
| | cosine_precision@1 | 0.9308 |
|
| | cosine_precision@3 | 0.4145 |
|
| | cosine_precision@5 | 0.267 |
|
| | cosine_precision@10 | 0.1414 |
|
| | cosine_recall@1 | 0.8009 |
|
| | cosine_recall@3 | 0.9314 |
|
| | cosine_recall@5 | 0.9558 |
|
| | cosine_recall@10 | 0.9744 |
|
| | **cosine_ndcg@10** | **0.9511** |
|
| | cosine_mrr@10 | 0.9512 |
|
| | cosine_map@100 | 0.9391 |
|
|
|
| <!--
|
| ## Bias, Risks and Limitations
|
|
|
| *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
|
| -->
|
|
|
| <!--
|
| ### Recommendations
|
|
|
| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
|
| -->
|
|
|
| ## Training Details
|
|
|
| ### Training Dataset
|
|
|
| #### quora-duplicates
|
|
|
| * Dataset: [quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb)
|
| * Size: 404,290 training samples
|
| * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
|
| * Approximate statistics based on the first 1000 samples:
|
| | | sentence1 | sentence2 | label |
|
| |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
|
| | type | string | string | int |
|
| | details | <ul><li>min: 6 tokens</li><li>mean: 15.73 tokens</li><li>max: 65 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.93 tokens</li><li>max: 85 tokens</li></ul> | <ul><li>0: ~61.60%</li><li>1: ~38.40%</li></ul> |
|
| * Samples:
|
| | sentence1 | sentence2 | label |
|
| |:----------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------|:---------------|
|
| | <code>How can Trump supporters claim he didn't mock a disabled reporter when there is live footage of him mocking a disabled reporter?</code> | <code>Why don't people actually watch the Trump video of him allegedly mocking a disabled reporter?</code> | <code>0</code> |
|
| | <code>Where can I get the best digital marketing course (online & offline) in India?</code> | <code>Which is the best digital marketing institute for professionals in India?</code> | <code>1</code> |
|
| | <code>What best two liner shayri?</code> | <code>What does "senile dementia, uncomplicated" mean in medical terms?</code> | <code>0</code> |
|
| * Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)
|
|
|
| ### Evaluation Dataset
|
|
|
| #### quora-duplicates
|
|
|
| * Dataset: [quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb)
|
| * Size: 404,290 evaluation samples
|
| * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
|
| * Approximate statistics based on the first 1000 samples:
|
| | | sentence1 | sentence2 | label |
|
| |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
|
| | type | string | string | int |
|
| | details | <ul><li>min: 6 tokens</li><li>mean: 16.14 tokens</li><li>max: 70 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.92 tokens</li><li>max: 74 tokens</li></ul> | <ul><li>0: ~60.10%</li><li>1: ~39.90%</li></ul> |
|
| * Samples:
|
| | sentence1 | sentence2 | label |
|
| |:--------------------------------------------------------|:-----------------------------------------------------------|:---------------|
|
| | <code>What are some must subscribe RSS feeds?</code> | <code>What are RSS feeds?</code> | <code>0</code> |
|
| | <code>How close are Madonna and Hillary Clinton?</code> | <code>Why do people say Hillary Clinton is a crook?</code> | <code>0</code> |
|
| | <code>Can you share best day of your life?</code> | <code>What is the Best Day of your life till date?</code> | <code>1</code> |
|
| * Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)
|
|
|
| ### Training Hyperparameters
|
| #### Non-Default Hyperparameters
|
|
|
| - `eval_strategy`: steps
|
| - `per_device_train_batch_size`: 64
|
| - `per_device_eval_batch_size`: 64
|
| - `num_train_epochs`: 1
|
| - `warmup_ratio`: 0.1
|
| - `fp16`: True
|
| - `batch_sampler`: no_duplicates
|
|
|
| #### All Hyperparameters
|
| <details><summary>Click to expand</summary>
|
|
|
| - `overwrite_output_dir`: False
|
| - `do_predict`: False
|
| - `eval_strategy`: steps
|
| - `prediction_loss_only`: True
|
| - `per_device_train_batch_size`: 64
|
| - `per_device_eval_batch_size`: 64
|
| - `per_gpu_train_batch_size`: None
|
| - `per_gpu_eval_batch_size`: None
|
| - `gradient_accumulation_steps`: 1
|
| - `eval_accumulation_steps`: None
|
| - `torch_empty_cache_steps`: None
|
| - `learning_rate`: 5e-05
|
| - `weight_decay`: 0.0
|
| - `adam_beta1`: 0.9
|
| - `adam_beta2`: 0.999
|
| - `adam_epsilon`: 1e-08
|
| - `max_grad_norm`: 1.0
|
| - `num_train_epochs`: 1
|
| - `max_steps`: -1
|
| - `lr_scheduler_type`: linear
|
| - `lr_scheduler_kwargs`: {}
|
| - `warmup_ratio`: 0.1
|
| - `warmup_steps`: 0
|
| - `log_level`: passive
|
| - `log_level_replica`: warning
|
| - `log_on_each_node`: True
|
| - `logging_nan_inf_filter`: True
|
| - `save_safetensors`: True
|
| - `save_on_each_node`: False
|
| - `save_only_model`: False
|
| - `restore_callback_states_from_checkpoint`: False
|
| - `no_cuda`: False
|
| - `use_cpu`: False
|
| - `use_mps_device`: False
|
| - `seed`: 42
|
| - `data_seed`: None
|
| - `jit_mode_eval`: False
|
| - `use_ipex`: False
|
| - `bf16`: False
|
| - `fp16`: True
|
| - `fp16_opt_level`: O1
|
| - `half_precision_backend`: auto
|
| - `bf16_full_eval`: False
|
| - `fp16_full_eval`: False
|
| - `tf32`: None
|
| - `local_rank`: 0
|
| - `ddp_backend`: None
|
| - `tpu_num_cores`: None
|
| - `tpu_metrics_debug`: False
|
| - `debug`: []
|
| - `dataloader_drop_last`: False
|
| - `dataloader_num_workers`: 0
|
| - `dataloader_prefetch_factor`: None
|
| - `past_index`: -1
|
| - `disable_tqdm`: False
|
| - `remove_unused_columns`: True
|
| - `label_names`: None
|
| - `load_best_model_at_end`: False
|
| - `ignore_data_skip`: False
|
| - `fsdp`: []
|
| - `fsdp_min_num_params`: 0
|
| - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
|
| - `fsdp_transformer_layer_cls_to_wrap`: None
|
| - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
|
| - `deepspeed`: None
|
| - `label_smoothing_factor`: 0.0
|
| - `optim`: adamw_torch
|
| - `optim_args`: None
|
| - `adafactor`: False
|
| - `group_by_length`: False
|
| - `length_column_name`: length
|
| - `ddp_find_unused_parameters`: None
|
| - `ddp_bucket_cap_mb`: None
|
| - `ddp_broadcast_buffers`: False
|
| - `dataloader_pin_memory`: True
|
| - `dataloader_persistent_workers`: False
|
| - `skip_memory_metrics`: True
|
| - `use_legacy_prediction_loop`: False
|
| - `push_to_hub`: False
|
| - `resume_from_checkpoint`: None
|
| - `hub_model_id`: None
|
| - `hub_strategy`: every_save
|
| - `hub_private_repo`: False
|
| - `hub_always_push`: False
|
| - `gradient_checkpointing`: False
|
| - `gradient_checkpointing_kwargs`: None
|
| - `include_inputs_for_metrics`: False
|
| - `eval_do_concat_batches`: True
|
| - `fp16_backend`: auto
|
| - `push_to_hub_model_id`: None
|
| - `push_to_hub_organization`: None
|
| - `mp_parameters`:
|
| - `auto_find_batch_size`: False
|
| - `full_determinism`: False
|
| - `torchdynamo`: None
|
| - `ray_scope`: last
|
| - `ddp_timeout`: 1800
|
| - `torch_compile`: False
|
| - `torch_compile_backend`: None
|
| - `torch_compile_mode`: None
|
| - `dispatch_batches`: None
|
| - `split_batches`: None
|
| - `include_tokens_per_second`: False
|
| - `include_num_input_tokens_seen`: False
|
| - `neftune_noise_alpha`: None
|
| - `optim_target_modules`: None
|
| - `batch_eval_metrics`: False
|
| - `eval_on_start`: False
|
| - `eval_use_gather_object`: False
|
| - `prompts`: None
|
| - `batch_sampler`: no_duplicates
|
| - `multi_dataset_batch_sampler`: proportional
|
|
|
| </details>
|
|
|
| ### Training Logs
|
| | Epoch | Step | Training Loss | Validation Loss | quora-duplicates_cosine_ap | quora-duplicates-dev_average_precision | cosine_ndcg@10 |
|
| |:------:|:----:|:-------------:|:---------------:|:--------------------------:|:--------------------------------------:|:--------------:|
|
| | 0 | 0 | - | - | 0.7458 | 0.4200 | 0.9390 |
|
| | 0.0640 | 100 | 2.5263 | - | - | - | - |
|
| | 0.1280 | 200 | 2.1489 | - | - | - | - |
|
| | 0.1599 | 250 | - | 1.8621 | 0.8433 | 0.3907 | 0.9329 |
|
| | 0.1919 | 300 | 2.0353 | - | - | - | - |
|
| | 0.2559 | 400 | 1.7831 | - | - | - | - |
|
| | 0.3199 | 500 | 1.8887 | 1.7744 | 0.8662 | 0.4924 | 0.9379 |
|
| | 0.3839 | 600 | 1.7814 | - | - | - | - |
|
| | 0.4479 | 700 | 1.7775 | - | - | - | - |
|
| | 0.4798 | 750 | - | 1.6468 | 0.8766 | 0.4945 | 0.9399 |
|
| | 0.5118 | 800 | 1.6835 | - | - | - | - |
|
| | 0.5758 | 900 | 1.6974 | - | - | - | - |
|
| | 0.6398 | 1000 | 1.5704 | 1.4925 | 0.8895 | 0.5283 | 0.9460 |
|
| | 0.7038 | 1100 | 1.6771 | - | - | - | - |
|
| | 0.7678 | 1200 | 1.619 | - | - | - | - |
|
| | 0.7997 | 1250 | - | 1.4311 | 0.8982 | 0.5252 | 0.9466 |
|
| | 0.8317 | 1300 | 1.6119 | - | - | - | - |
|
| | 0.8957 | 1400 | 1.6043 | - | - | - | - |
|
| | 0.9597 | 1500 | 1.6848 | 1.4070 | 0.8988 | 0.5483 | 0.9511 |
|
|
|
|
|
| ### Framework Versions
|
| - Python: 3.9.18
|
| - Sentence Transformers: 3.4.1
|
| - Transformers: 4.44.2
|
| - PyTorch: 2.2.1+cu121
|
| - Accelerate: 1.3.0
|
| - Datasets: 2.19.0
|
| - 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",
|
| }
|
| ```
|
|
|
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