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stsb-distilbert-base-quora-duplicate-questions

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 ---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated
base_model: sentence-transformers/stsb-distilbert-base
metrics:
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- manhattan_accuracy
- manhattan_accuracy_threshold
- manhattan_f1
- manhattan_f1_threshold
- manhattan_precision
- manhattan_recall
- manhattan_ap
- euclidean_accuracy
- euclidean_accuracy_threshold
- euclidean_f1
- euclidean_f1_threshold
- euclidean_precision
- euclidean_recall
- euclidean_ap
- dot_accuracy
- dot_accuracy_threshold
- dot_f1
- dot_f1_threshold
- dot_precision
- dot_recall
- dot_ap
- max_accuracy
- max_accuracy_threshold
- max_f1
- max_f1_threshold
- max_precision
- max_recall
- max_ap
- 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
- dot_accuracy@1
- dot_accuracy@3
- dot_accuracy@5
- dot_accuracy@10
- dot_precision@1
- dot_precision@3
- dot_precision@5
- dot_precision@10
- dot_recall@1
- dot_recall@3
- dot_recall@5
- dot_recall@10
- dot_ndcg@10
- dot_mrr@10
- dot_map@100
widget:
- source_sentence: How porn is made?
sentences:
- How is porn made?
- How do you study before a test?
- What is the best book for afcat?
- source_sentence: Is WW3 inevitable?
sentences:
- How close to WW3 are we?
- Is it ok not to know everything?
- How can I get good marks on my exam?
- source_sentence: How do stop smoking?
sentences:
- How did you quit/stop smoking?
- How can I gain weight naturally?
- What movie is the best movie of 2016?
- source_sentence: What is astrology?
sentences:
- What really is astrology?
- How do I control blood pressure?
- How should I reduce weight easily?
- source_sentence: What is SMS API?
sentences:
- What is an SMS API?
- How will Sound travel in SPACE?
- Do we live inside a black hole?
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on sentence-transformers/stsb-distilbert-base
results:
- task:
type: binary-classification
name: Binary Classification
dataset:
name: Unknown
type: unknown
metrics:
- type: cosine_accuracy
value: 0.770712179816613
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.8169694542884827
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.7086398522340053
name: Cosine F1
- type: cosine_f1_threshold
value: 0.7420324087142944
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.6032968224704479
name: Cosine Precision
- type: cosine_recall
value: 0.8585539007639479
name: Cosine Recall
- type: cosine_ap
value: 0.7191176594498068
name: Cosine Ap
- type: manhattan_accuracy
value: 0.7729301344296882
name: Manhattan Accuracy
- type: manhattan_accuracy_threshold
value: 181.4663848876953
name: Manhattan Accuracy Threshold
- type: manhattan_f1
value: 0.7082838527457715
name: Manhattan F1
- type: manhattan_f1_threshold
value: 222.911865234375
name: Manhattan F1 Threshold
- type: manhattan_precision
value: 0.6063303659742829
name: Manhattan Precision
- type: manhattan_recall
value: 0.8514545875453353
name: Manhattan Recall
- type: manhattan_ap
value: 0.7188011305084623
name: Manhattan Ap
- type: euclidean_accuracy
value: 0.7736333883313948
name: Euclidean Accuracy
- type: euclidean_accuracy_threshold
value: 8.356552124023438
name: Euclidean Accuracy Threshold
- type: euclidean_f1
value: 0.7088200276731988
name: Euclidean F1
- type: euclidean_f1_threshold
value: 10.092880249023438
name: Euclidean F1 Threshold
- type: euclidean_precision
value: 0.6079037421348935
name: Euclidean Precision
- type: euclidean_recall
value: 0.8499112585847673
name: Euclidean Recall
- type: euclidean_ap
value: 0.719131590718056
name: Euclidean Ap
- type: dot_accuracy
value: 0.7441508209136891
name: Dot Accuracy
- type: dot_accuracy_threshold
value: 168.56625366210938
name: Dot Accuracy Threshold
- type: dot_f1
value: 0.6831510249103777
name: Dot F1
- type: dot_f1_threshold
value: 142.45849609375
name: Dot F1 Threshold
- type: dot_precision
value: 0.5665209879052749
name: Dot Precision
- type: dot_recall
value: 0.8602515626205726
name: Dot Recall
- type: dot_ap
value: 0.6693622133717865
name: Dot Ap
- type: max_accuracy
value: 0.7736333883313948
name: Max Accuracy
- type: max_accuracy_threshold
value: 181.4663848876953
name: Max Accuracy Threshold
- type: max_f1
value: 0.7088200276731988
name: Max F1
- type: max_f1_threshold
value: 222.911865234375
name: Max F1 Threshold
- type: max_precision
value: 0.6079037421348935
name: Max Precision
- type: max_recall
value: 0.8602515626205726
name: Max Recall
- type: max_ap
value: 0.719131590718056
name: Max Ap
- task:
type: paraphrase-mining
name: Paraphrase Mining
dataset:
name: dev
type: dev
metrics:
- type: average_precision
value: 0.47803306271270435
name: Average Precision
- type: f1
value: 0.5119182746878547
name: F1
- type: precision
value: 0.4683281412253375
name: Precision
- type: recall
value: 0.5644555694618273
name: Recall
- type: threshold
value: 0.8193174600601196
name: Threshold
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: Unknown
type: unknown
metrics:
- type: cosine_accuracy@1
value: 0.9654
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9904
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9948
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9974
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.9654
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.43553333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.28064
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.14934
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.8251379240296788
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.9549051140803786
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9757885342898082
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9898260744103871
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9786162291363164
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9785615873015873
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9713888565523412
name: Cosine Map@100
- type: dot_accuracy@1
value: 0.9512
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.985
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.9914
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.9964
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.9512
name: Dot Precision@1
- type: dot_precision@3
value: 0.4303333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.2788
name: Dot Precision@5
- type: dot_precision@10
value: 0.14896
name: Dot Precision@10
- type: dot_recall@1
value: 0.8119095906963455
name: Dot Recall@1
- type: dot_recall@3
value: 0.9459636855089498
name: Dot Recall@3
- type: dot_recall@5
value: 0.9708092557905298
name: Dot Recall@5
- type: dot_recall@10
value: 0.9883617291912786
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.9702609044345125
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.9693138888888887
name: Dot Mrr@10
- type: dot_map@100
value: 0.9599586870108953
name: Dot 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). 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)
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 768 tokens
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **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("tomaarsen/stsb-distilbert-base-quora-duplicate-questions")
# Run inference
sentences = [
"What is a fetish?",
"What's a fetish?",
"Is it good to read sex stories?",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
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<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
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### Out-of-Scope Use
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## Evaluation
### Metrics
#### Binary Classification
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
| Metric | Value |
|:-----------------------------|:-----------|
| **cosine_accuracy** | **0.7707** |
| cosine_accuracy_threshold | 0.817 |
| cosine_f1 | 0.7086 |
| cosine_f1_threshold | 0.742 |
| cosine_precision | 0.6033 |
| cosine_recall | 0.8586 |
| cosine_ap | 0.7191 |
| manhattan_accuracy | 0.7729 |
| manhattan_accuracy_threshold | 181.4664 |
| manhattan_f1 | 0.7083 |
| manhattan_f1_threshold | 222.9119 |
| manhattan_precision | 0.6063 |
| manhattan_recall | 0.8515 |
| manhattan_ap | 0.7188 |
| euclidean_accuracy | 0.7736 |
| euclidean_accuracy_threshold | 8.3566 |
| euclidean_f1 | 0.7088 |
| euclidean_f1_threshold | 10.0929 |
| euclidean_precision | 0.6079 |
| euclidean_recall | 0.8499 |
| euclidean_ap | 0.7191 |
| dot_accuracy | 0.7442 |
| dot_accuracy_threshold | 168.5663 |
| dot_f1 | 0.6832 |
| dot_f1_threshold | 142.4585 |
| dot_precision | 0.5665 |
| dot_recall | 0.8603 |
| dot_ap | 0.6694 |
| max_accuracy | 0.7736 |
| max_accuracy_threshold | 181.4664 |
| max_f1 | 0.7088 |
| max_f1_threshold | 222.9119 |
| max_precision | 0.6079 |
| max_recall | 0.8603 |
| max_ap | 0.7191 |
#### Paraphrase Mining
* Dataset: `dev`
* Evaluated with [<code>ParaphraseMiningEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.ParaphraseMiningEvaluator)
| Metric | Value |
|:----------------------|:----------|
| **average_precision** | **0.478** |
| f1 | 0.5119 |
| precision | 0.4683 |
| recall | 0.5645 |
| threshold | 0.8193 |
#### Information Retrieval
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.9654 |
| cosine_accuracy@3 | 0.9904 |
| cosine_accuracy@5 | 0.9948 |
| cosine_accuracy@10 | 0.9974 |
| cosine_precision@1 | 0.9654 |
| cosine_precision@3 | 0.4355 |
| cosine_precision@5 | 0.2806 |
| cosine_precision@10 | 0.1493 |
| cosine_recall@1 | 0.8251 |
| cosine_recall@3 | 0.9549 |
| cosine_recall@5 | 0.9758 |
| cosine_recall@10 | 0.9898 |
| cosine_ndcg@10 | 0.9786 |
| cosine_mrr@10 | 0.9786 |
| **cosine_map@100** | **0.9714** |
| dot_accuracy@1 | 0.9512 |
| dot_accuracy@3 | 0.985 |
| dot_accuracy@5 | 0.9914 |
| dot_accuracy@10 | 0.9964 |
| dot_precision@1 | 0.9512 |
| dot_precision@3 | 0.4303 |
| dot_precision@5 | 0.2788 |
| dot_precision@10 | 0.149 |
| dot_recall@1 | 0.8119 |
| dot_recall@3 | 0.946 |
| dot_recall@5 | 0.9708 |
| dot_recall@10 | 0.9884 |
| dot_ndcg@10 | 0.9703 |
| dot_mrr@10 | 0.9693 |
| dot_map@100 | 0.96 |
<!--
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 207,326 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 | label |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------|
| type | string | string | int |
| details | <ul><li>min: 6 tokens</li><li>mean: 13.75 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.74 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>1: ~100.00%</li></ul> |
* Samples:
| sentence_0 | sentence_1 | label |
|:------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>How do I improve writing skill by myself?</code> | <code>How can I improve writing skills?</code> | <code>1</code> |
| <code>Is it best to switch to Node.js from PHP?</code> | <code>Should I switch to Node.js or continue using PHP?</code> | <code>1</code> |
| <code>What do Hillary Clinton's supporters say when confronted with all her lies and scandals?</code> | <code>What do Clinton supporters say when confronted with her scandals such as the emails and 'Clinton Cash'?</code> | <code>1</code> |
* Loss: [<code>sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- per_device_train_batch_size: 64
- per_device_eval_batch_size: 64
- num_train_epochs: 1
- round_robin_sampler: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- overwrite_output_dir: False
- do_predict: False
- prediction_loss_only: False
- 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
- learning_rate: 5e-05
- weight_decay: 0.0
- adam_beta1: 0.9
- adam_beta2: 0.999
- adam_epsilon: 1e-08
- max_grad_norm: 1
- num_train_epochs: 1
- max_steps: -1
- lr_scheduler_type: linear
- lr_scheduler_kwargs: {}
- warmup_ratio: 0.0
- 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
- 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: False
- 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}
- 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: None
- 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
- 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
- round_robin_sampler: True
</details>
### Training Logs
| Epoch | Step | Training Loss | cosine_accuracy | cosine_map@100 | dev_average_precision |
|:------:|:----:|:-------------:|:---------------:|:--------------:|:---------------------:|
| 0 | 0 | - | 0.7661 | 0.9371 | 0.4137 |
| 0.1543 | 500 | 0.1055 | 0.7632 | 0.9620 | 0.4731 |
| 0.3086 | 1000 | 0.0677 | 0.7608 | 0.9675 | 0.4732 |
| 0.4630 | 1500 | 0.0612 | 0.7663 | 0.9710 | 0.4856 |
| 0.6173 | 2000 | 0.0584 | 0.7719 | 0.9693 | 0.4925 |
| 0.7716 | 2500 | 0.0506 | 0.7714 | 0.9709 | 0.4808 |
| 0.9259 | 3000 | 0.0488 | 0.7708 | 0.9713 | 0.4784 |
| 1.0 | 3240 | - | 0.7707 | 0.9714 | 0.4780 |
### Framework Versions
- Python: 3.11.6
- Sentence Transformers: 2.7.0.dev0
- Transformers: 4.39.3
- PyTorch: 2.1.0+cu121
- Accelerate: 0.26.1
- Datasets: 2.18.0
- Tokenizers: 0.15.2
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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