sentence-transformers safetensors distilbert sentence-similarity feature-extraction loss:MultipleNegativesRankingLoss loss:ContrastiveLoss en arxiv:1908.10084 arxiv:1705.00652 base_model:sentence-transformers/stsb-distilbert-base base_model:finetune:sentence-transformers/stsb-distilbert-base model-index co2_eq_emissions autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: sentence-transformers/stsb-distilbert-base
language:
- en
library_name: sentence-transformers
metrics:
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- dot_accuracy
- dot_accuracy_threshold
- dot_f1
- dot_f1_threshold
- dot_precision
- dot_recall
- dot_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
- 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
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- loss:MultipleNegativesRankingLoss
- loss:ContrastiveLoss
widget:
- source_sentence: What is Mindset?
  sentences:
  - What is a mindset?
  - Can you eat only once a day?
  - Is law a good career choice?
- source_sentence: Is a queef real?
  sentences:
  - Is "G" based on real events?
  - What is the entire court process?
  - How do I reduce my weight?
- source_sentence: Is Cicret a scam?
  sentences:
  - Is the Cicret Bracelet a scam?
  - Was World War II Inevitable?
  - What are some of the best photos?
- source_sentence: What is Planet X?
  sentences:
  - Do planet X exist?
  - What are the best C++ books?
  - How can I lose my weight fast?
- source_sentence: How fast is fast?
  sentences:
  - How does light travel so fast?
  - How do I copyright my books?
  - What is a black hole made of?
co2_eq_emissions:
  emissions: 32.724475965905576
  energy_consumed: 0.08418911136527617
  source: codecarbon
  training_type: fine-tuning
  on_cloud: false
  cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
  ram_total_size: 31.777088165283203
  hours_used: 0.399
  hardware_used: 1 x NVIDIA GeForce RTX 3090
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: 0.846
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.7969297170639038
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.7791495198902607
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.7139598727226257
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 0.6977886977886978
      name: Cosine Precision
    - type: cosine_recall
      value: 0.8819875776397516
      name: Cosine Recall
    - type: cosine_ap
      value: 0.8230449963294564
      name: Cosine Ap
    - type: dot_accuracy
      value: 0.843
      name: Dot Accuracy
    - type: dot_accuracy_threshold
      value: 151.2908477783203
      name: Dot Accuracy Threshold
    - type: dot_f1
      value: 0.7660818713450294
      name: Dot F1
    - type: dot_f1_threshold
      value: 143.77838134765625
      name: Dot F1 Threshold
    - type: dot_precision
      value: 0.7237569060773481
      name: Dot Precision
    - type: dot_recall
      value: 0.8136645962732919
      name: Dot Recall
    - type: dot_ap
      value: 0.7946044629726107
      name: Dot Ap
    - type: manhattan_accuracy
      value: 0.838
      name: Manhattan Accuracy
    - type: manhattan_accuracy_threshold
      value: 194.99119567871094
      name: Manhattan Accuracy Threshold
    - type: manhattan_f1
      value: 0.7704081632653061
      name: Manhattan F1
    - type: manhattan_f1_threshold
      value: 247.49777221679688
      name: Manhattan F1 Threshold
    - type: manhattan_precision
      value: 0.6536796536796536
      name: Manhattan Precision
    - type: manhattan_recall
      value: 0.937888198757764
      name: Manhattan Recall
    - type: manhattan_ap
      value: 0.8149715271935773
      name: Manhattan Ap
    - type: euclidean_accuracy
      value: 0.841
      name: Euclidean Accuracy
    - type: euclidean_accuracy_threshold
      value: 9.02225112915039
      name: Euclidean Accuracy Threshold
    - type: euclidean_f1
      value: 0.7703889585947302
      name: Euclidean F1
    - type: euclidean_f1_threshold
      value: 11.385245323181152
      name: Euclidean F1 Threshold
    - type: euclidean_precision
      value: 0.6463157894736842
      name: Euclidean Precision
    - type: euclidean_recall
      value: 0.953416149068323
      name: Euclidean Recall
    - type: euclidean_ap
      value: 0.8152967320117391
      name: Euclidean Ap
    - type: max_accuracy
      value: 0.846
      name: Max Accuracy
    - type: max_accuracy_threshold
      value: 194.99119567871094
      name: Max Accuracy Threshold
    - type: max_f1
      value: 0.7791495198902607
      name: Max F1
    - type: max_f1_threshold
      value: 247.49777221679688
      name: Max F1 Threshold
    - type: max_precision
      value: 0.7237569060773481
      name: Max Precision
    - type: max_recall
      value: 0.953416149068323
      name: Max Recall
    - type: max_ap
      value: 0.8230449963294564
      name: Max Ap
  - task:
      type: paraphrase-mining
      name: Paraphrase Mining
    dataset:
      name: quora duplicates dev
      type: quora-duplicates-dev
    metrics:
    - type: average_precision
      value: 0.5888649029434471
      name: Average Precision
    - type: f1
      value: 0.5761652140962487
      name: F1
    - type: precision
      value: 0.5477552123204396
      name: Precision
    - type: recall
      value: 0.6076834690513064
      name: Recall
    - type: threshold
      value: 0.7728720009326935
      name: Threshold
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: cosine_accuracy@1
      value: 0.963
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9906
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9944
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9982
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.963
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.4285333333333333
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.27568000000000004
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.14494
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.8299562338609103
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9590366552956846
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9806221849555673
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9925738410935468
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9784033087450696
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9771579365079368
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9709189650394419
      name: Cosine Map@100
    - type: dot_accuracy@1
      value: 0.9514
      name: Dot Accuracy@1
    - type: dot_accuracy@3
      value: 0.9852
      name: Dot Accuracy@3
    - type: dot_accuracy@5
      value: 0.991
      name: Dot Accuracy@5
    - type: dot_accuracy@10
      value: 0.9968
      name: Dot Accuracy@10
    - type: dot_precision@1
      value: 0.9514
      name: Dot Precision@1
    - type: dot_precision@3
      value: 0.4247333333333334
      name: Dot Precision@3
    - type: dot_precision@5
      value: 0.27364
      name: Dot Precision@5
    - type: dot_precision@10
      value: 0.14458000000000001
      name: Dot Precision@10
    - type: dot_recall@1
      value: 0.8194380520427287
      name: Dot Recall@1
    - type: dot_recall@3
      value: 0.9520212390452685
      name: Dot Recall@3
    - type: dot_recall@5
      value: 0.9755502441186265
      name: Dot Recall@5
    - type: dot_recall@10
      value: 0.9910547306614953
      name: Dot Recall@10
    - type: dot_ndcg@10
      value: 0.9715023430522326
      name: Dot Ndcg@10
    - type: dot_mrr@10
      value: 0.9692583333333334
      name: Dot Mrr@10
    - type: dot_map@100
      value: 0.961739772177385
      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) on the [mnrl](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) and [cl](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) datasets. 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 82ad392c08f81be9be9bf065339670b23f2e1493 -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Datasets:**
    - [mnrl](https://huggingface.co/datasets/sentence-transformers/quora-duplicates)
    - [cl](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("tomaarsen/stsb-distilbert-base-mnrl-cl-multi")
# Run inference
sentences = [
    'How fast is fast?',
    'How does light travel so fast?',
    'How do I copyright my books?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings)
print(similarities.shape)
# [3, 3]
```

<!--
### 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>

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### Out-of-Scope Use

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## Evaluation

### Metrics

#### Binary Classification
* Dataset: `quora-duplicates`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)

| Metric                       | Value     |
|:-----------------------------|:----------|
| cosine_accuracy              | 0.846     |
| cosine_accuracy_threshold    | 0.7969    |
| cosine_f1                    | 0.7791    |
| cosine_f1_threshold          | 0.714     |
| cosine_precision             | 0.6978    |
| cosine_recall                | 0.882     |
| cosine_ap                    | 0.823     |
| dot_accuracy                 | 0.843     |
| dot_accuracy_threshold       | 151.2908  |
| dot_f1                       | 0.7661    |
| dot_f1_threshold             | 143.7784  |
| dot_precision                | 0.7238    |
| dot_recall                   | 0.8137    |
| dot_ap                       | 0.7946    |
| manhattan_accuracy           | 0.838     |
| manhattan_accuracy_threshold | 194.9912  |
| manhattan_f1                 | 0.7704    |
| manhattan_f1_threshold       | 247.4978  |
| manhattan_precision          | 0.6537    |
| manhattan_recall             | 0.9379    |
| manhattan_ap                 | 0.815     |
| euclidean_accuracy           | 0.841     |
| euclidean_accuracy_threshold | 9.0223    |
| euclidean_f1                 | 0.7704    |
| euclidean_f1_threshold       | 11.3852   |
| euclidean_precision          | 0.6463    |
| euclidean_recall             | 0.9534    |
| euclidean_ap                 | 0.8153    |
| max_accuracy                 | 0.846     |
| max_accuracy_threshold       | 194.9912  |
| max_f1                       | 0.7791    |
| max_f1_threshold             | 247.4978  |
| max_precision                | 0.7238    |
| max_recall                   | 0.9534    |
| **max_ap**                   | **0.823** |

#### Paraphrase Mining
* Dataset: `quora-duplicates-dev`
* Evaluated with [<code>ParaphraseMiningEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.ParaphraseMiningEvaluator)

| Metric                | Value      |
|:----------------------|:-----------|
| **average_precision** | **0.5889** |
| f1                    | 0.5762     |
| precision             | 0.5478     |
| recall                | 0.6077     |
| threshold             | 0.7729     |

#### Information Retrieval

* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.963      |
| cosine_accuracy@3   | 0.9906     |
| cosine_accuracy@5   | 0.9944     |
| cosine_accuracy@10  | 0.9982     |
| cosine_precision@1  | 0.963      |
| cosine_precision@3  | 0.4285     |
| cosine_precision@5  | 0.2757     |
| cosine_precision@10 | 0.1449     |
| cosine_recall@1     | 0.83       |
| cosine_recall@3     | 0.959      |
| cosine_recall@5     | 0.9806     |
| cosine_recall@10    | 0.9926     |
| cosine_ndcg@10      | 0.9784     |
| cosine_mrr@10       | 0.9772     |
| **cosine_map@100**  | **0.9709** |
| dot_accuracy@1      | 0.9514     |
| dot_accuracy@3      | 0.9852     |
| dot_accuracy@5      | 0.991      |
| dot_accuracy@10     | 0.9968     |
| dot_precision@1     | 0.9514     |
| dot_precision@3     | 0.4247     |
| dot_precision@5     | 0.2736     |
| dot_precision@10    | 0.1446     |
| dot_recall@1        | 0.8194     |
| dot_recall@3        | 0.952      |
| dot_recall@5        | 0.9756     |
| dot_recall@10       | 0.9911     |
| dot_ndcg@10         | 0.9715     |
| dot_mrr@10          | 0.9693     |
| dot_map@100         | 0.9617     |

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### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details

### Training Datasets

#### mnrl

* Dataset: [mnrl](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb)
* Size: 100,000 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 6 tokens</li><li>mean: 13.85 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.65 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.76 tokens</li><li>max: 64 tokens</li></ul> |
* Samples:
  | anchor                                                                          | positive                                                                                       | negative                                                                                                         |
  |:--------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------|
  | <code>Why in India do we not have one on one political debate as in USA?</code> | <code>Why cant we have a public debate between politicians in India like the one in US?</code> | <code>Can people on Quora stop India Pakistan debate? We are sick and tired seeing this everyday in bulk?</code> |
  | <code>What is OnePlus One?</code>                                               | <code>How is oneplus one?</code>                                                               | <code>Why is OnePlus One so good?</code>                                                                         |
  | <code>Does our mind control our emotions?</code>                                | <code>How do smart and successful people control their emotions?</code>                        | <code>How can I control my positive emotions for the people whom I love but they don't care about me?</code>     |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### cl

* Dataset: [cl](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb)
* Size: 100,000 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: 15.3 tokens</li><li>max: 57 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.66 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>0: ~62.00%</li><li>1: ~38.00%</li></ul> |
* Samples:
  | sentence1                                                                              | sentence2                                                                                             | label          |
  |:---------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------|:---------------|
  | <code>What is the step by step guide to invest in share market in india?</code>        | <code>What is the step by step guide to invest in share market?</code>                                | <code>0</code> |
  | <code>What is the story of Kohinoor (Koh-i-Noor) Diamond?</code>                       | <code>What would happen if the Indian government stole the Kohinoor (Koh-i-Noor) diamond back?</code> | <code>0</code> |
  | <code>How can I increase the speed of my internet connection while using a VPN?</code> | <code>How can Internet speed be increased by hacking through DNS?</code>                              | <code>0</code> |
* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/losses.html#contrastiveloss) with these parameters:
  ```json
  {
      "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
      "margin": 0.5,
      "size_average": true
  }
  ```

### Evaluation Datasets

#### mnrl

* Dataset: [mnrl](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb)
* Size: 1,000 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                         | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                           | string                                                                            |
  | details | <ul><li>min: 7 tokens</li><li>mean: 13.84 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.8 tokens</li><li>max: 38 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.71 tokens</li><li>max: 56 tokens</li></ul> |
* Samples:
  | anchor                                                                                                   | positive                                                                         | negative                                                                                                                                                                                                                                                                          |
  |:---------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Which programming language is best for developing low-end games?</code>                            | <code>What coding language should I learn first for making games?</code>         | <code>I am entering the world of video game programming and want to know what language I should learn? Because there are so many languages ​​I do not know which one to start with. Can you recommend a language that's easy to learn and can be used with many platforms?</code> |
  | <code>Was it appropriate for Meryl Streep to use her Golden Globes speech to attack Donald Trump?</code> | <code>Should Meryl Streep be using her position to attack the president?</code>  | <code>Why did Kelly Ann Conway say that Meryl Streep incited peoples worst feelings?</code>                                                                                                                                                                                       |
  | <code>Where can I found excellent commercial fridges in Sydney?</code>                                   | <code>Where can I found impressive range of commercial fridges in Sydney?</code> | <code>What is the best grocery delivery service in Sydney?</code>                                                                                                                                                                                                                 |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

#### cl

* Dataset: [cl](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb)
* Size: 1,000 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: 5 tokens</li><li>mean: 15.59 tokens</li><li>max: 59 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.65 tokens</li><li>max: 76 tokens</li></ul> | <ul><li>0: ~63.40%</li><li>1: ~36.60%</li></ul> |
* Samples:
  | sentence1                                                                 | sentence2                                                                                                | label          |
  |:--------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------|:---------------|
  | <code>What should I ask my friend to get from UK to India?</code>         | <code>What is the process of getting a surgical residency in UK after completing MBBS from India?</code> | <code>0</code> |
  | <code>How can I learn hacking for free?</code>                            | <code>How can I learn to hack seriously?</code>                                                          | <code>1</code> |
  | <code>Which is the best website to learn programming language C++?</code> | <code>Which is the best website to learn C++ Programming language for free?</code>                       | <code>0</code> |
* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/losses.html#contrastiveloss) with these parameters:
  ```json
  {
      "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
      "margin": 0.5,
      "size_average": true
  }
  ```

### 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`: 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`: 1.0
- `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
- `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`: 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
- `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_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | cl loss | mnrl loss | cosine_map@100 | quora-duplicates-dev_average_precision | quora-duplicates_max_ap |
|:------:|:----:|:-------------:|:-------:|:---------:|:--------------:|:--------------------------------------:|:-----------------------:|
| 0      | 0    | -             | -       | -         | 0.9245         | 0.4200                                 | 0.6890                  |
| 0.0320 | 100  | 0.1634        | -       | -         | -              | -                                      | -                       |
| 0.0640 | 200  | 0.1206        | -       | -         | -              | -                                      | -                       |
| 0.0800 | 250  | -             | 0.0190  | 0.1469    | 0.9530         | 0.5068                                 | 0.7354                  |
| 0.0960 | 300  | 0.1036        | -       | -         | -              | -                                      | -                       |
| 0.1280 | 400  | 0.0836        | -       | -         | -              | -                                      | -                       |
| 0.1599 | 500  | 0.0918        | 0.0180  | 0.1008    | 0.9553         | 0.5259                                 | 0.7643                  |
| 0.1919 | 600  | 0.0784        | -       | -         | -              | -                                      | -                       |
| 0.2239 | 700  | 0.0656        | -       | -         | -              | -                                      | -                       |
| 0.2399 | 750  | -             | 0.0177  | 0.0905    | 0.9593         | 0.5305                                 | 0.7686                  |
| 0.2559 | 800  | 0.0593        | -       | -         | -              | -                                      | -                       |
| 0.2879 | 900  | 0.0534        | -       | -         | -              | -                                      | -                       |
| 0.3199 | 1000 | 0.0612        | 0.0161  | 0.0736    | 0.9642         | 0.5512                                 | 0.7881                  |
| 0.3519 | 1100 | 0.0572        | -       | -         | -              | -                                      | -                       |
| 0.3839 | 1200 | 0.06          | -       | -         | -              | -                                      | -                       |
| 0.3999 | 1250 | -             | 0.0158  | 0.0641    | 0.9649         | 0.5567                                 | 0.7983                  |
| 0.4159 | 1300 | 0.0565        | -       | -         | -              | -                                      | -                       |
| 0.4479 | 1400 | 0.0565        | -       | -         | -              | -                                      | -                       |
| 0.4798 | 1500 | 0.0475        | 0.0154  | 0.0578    | 0.9645         | 0.5614                                 | 0.8062                  |
| 0.5118 | 1600 | 0.0596        | -       | -         | -              | -                                      | -                       |
| 0.5438 | 1700 | 0.0509        | -       | -         | -              | -                                      | -                       |
| 0.5598 | 1750 | -             | 0.0150  | 0.0525    | 0.9674         | 0.5762                                 | 0.8092                  |
| 0.5758 | 1800 | 0.0403        | -       | -         | -              | -                                      | -                       |
| 0.6078 | 1900 | 0.0431        | -       | -         | -              | -                                      | -                       |
| 0.6398 | 2000 | 0.0481        | 0.0150  | 0.0531    | 0.9689         | 0.5824                                 | 0.8128                  |
| 0.6718 | 2100 | 0.05          | -       | -         | -              | -                                      | -                       |
| 0.7038 | 2200 | 0.0468        | -       | -         | -              | -                                      | -                       |
| 0.7198 | 2250 | -             | 0.0146  | 0.0486    | 0.9684         | 0.5756                                 | 0.8195                  |
| 0.7358 | 2300 | 0.0436        | -       | -         | -              | -                                      | -                       |
| 0.7678 | 2400 | 0.0409        | -       | -         | -              | -                                      | -                       |
| 0.7997 | 2500 | 0.0391        | 0.0145  | 0.0454    | 0.9705         | 0.5822                                 | 0.8190                  |
| 0.8317 | 2600 | 0.0412        | -       | -         | -              | -                                      | -                       |
| 0.8637 | 2700 | 0.0373        | -       | -         | -              | -                                      | -                       |
| 0.8797 | 2750 | -             | 0.0143  | 0.0451    | 0.9705         | 0.5889                                 | 0.8229                  |
| 0.8957 | 2800 | 0.0428        | -       | -         | -              | -                                      | -                       |
| 0.9277 | 2900 | 0.0419        | -       | -         | -              | -                                      | -                       |
| 0.9597 | 3000 | 0.0376        | 0.0143  | 0.0435    | 0.9709         | 0.5889                                 | 0.8230                  |
| 0.9917 | 3100 | 0.0366        | -       | -         | -              | -                                      | -                       |


### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.084 kWh
- **Carbon Emitted**: 0.033 kg of CO2
- **Hours Used**: 0.399 hours

### Training Hardware
- **On Cloud**: No
- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
- **RAM Size**: 31.78 GB

### Framework Versions
- Python: 3.11.6
- Sentence Transformers: 3.0.0.dev0
- Transformers: 4.41.0.dev0
- PyTorch: 2.3.0+cu121
- Accelerate: 0.26.1
- Datasets: 2.18.0
- 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",
}
```

#### 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}
}
```

#### ContrastiveLoss
```bibtex
@inproceedings{hadsell2006dimensionality,
    author={Hadsell, R. and Chopra, S. and LeCun, Y.},
    booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)}, 
    title={Dimensionality Reduction by Learning an Invariant Mapping}, 
    year={2006},
    volume={2},
    number={},
    pages={1735-1742},
    doi={10.1109/CVPR.2006.100}
}
```

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