README.md

---
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]
```

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## 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     |

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## 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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