---
language:
- en
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- loss:OnlineContrastiveLoss
base_model: sentence-transformers/stsb-distilbert-base
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
widget:
- source_sentence: Why did he go MIA?
  sentences:
  - Why did Yahoo kill Konfabulator?
  - Why do people get angry with me?
  - What are the best waterproof guns?
- source_sentence: Who is a soulmate?
  sentences:
  - Is she the “One”?
  - Who is Pakistan's biggest enemy?
  - Will smoking weed help with my anxiety?
- source_sentence: Is this poem good?
  sentences:
  - Is my poem any good?
  - How can I become a good speaker?
  - What is feminism?
- source_sentence: Who invented Yoga?
  sentences:
  - How was yoga invented?
  - Who owns this number 3152150252?
  - What is Dynamics CRM Services?
- source_sentence: Is stretching bad?
  sentences:
  - Is stretching good for you?
  - If i=0; what will i=i++ do to i?
  - What is the Output of this C program ?
pipeline_tag: sentence-similarity
co2_eq_emissions:
  emissions: 15.707175691967695
  energy_consumed: 0.040409299905757354
  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: 0.202
  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: 0.86
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.8104104995727539
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.8250591016548463
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.7247534394264221
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 0.7347368421052631
      name: Cosine Precision
    - type: cosine_recall
      value: 0.9407008086253369
      name: Cosine Recall
    - type: cosine_ap
      value: 0.887247904332921
      name: Cosine Ap
    - type: dot_accuracy
      value: 0.828
      name: Dot Accuracy
    - type: dot_accuracy_threshold
      value: 157.35491943359375
      name: Dot Accuracy Threshold
    - type: dot_f1
      value: 0.7898550724637681
      name: Dot F1
    - type: dot_f1_threshold
      value: 145.7113037109375
      name: Dot F1 Threshold
    - type: dot_precision
      value: 0.7155361050328227
      name: Dot Precision
    - type: dot_recall
      value: 0.8814016172506739
      name: Dot Recall
    - type: dot_ap
      value: 0.8369433397850002
      name: Dot Ap
    - type: manhattan_accuracy
      value: 0.868
      name: Manhattan Accuracy
    - type: manhattan_accuracy_threshold
      value: 208.00347900390625
      name: Manhattan Accuracy Threshold
    - type: manhattan_f1
      value: 0.8307692307692308
      name: Manhattan F1
    - type: manhattan_f1_threshold
      value: 208.00347900390625
      name: Manhattan F1 Threshold
    - type: manhattan_precision
      value: 0.7921760391198044
      name: Manhattan Precision
    - type: manhattan_recall
      value: 0.8733153638814016
      name: Manhattan Recall
    - type: manhattan_ap
      value: 0.8868217413983182
      name: Manhattan Ap
    - type: euclidean_accuracy
      value: 0.867
      name: Euclidean Accuracy
    - type: euclidean_accuracy_threshold
      value: 9.269388198852539
      name: Euclidean Accuracy Threshold
    - type: euclidean_f1
      value: 0.8301404853128991
      name: Euclidean F1
    - type: euclidean_f1_threshold
      value: 9.525729179382324
      name: Euclidean F1 Threshold
    - type: euclidean_precision
      value: 0.7888349514563107
      name: Euclidean Precision
    - type: euclidean_recall
      value: 0.876010781671159
      name: Euclidean Recall
    - type: euclidean_ap
      value: 0.8884154240019244
      name: Euclidean Ap
    - type: max_accuracy
      value: 0.868
      name: Max Accuracy
    - type: max_accuracy_threshold
      value: 208.00347900390625
      name: Max Accuracy Threshold
    - type: max_f1
      value: 0.8307692307692308
      name: Max F1
    - type: max_f1_threshold
      value: 208.00347900390625
      name: Max F1 Threshold
    - type: max_precision
      value: 0.7921760391198044
      name: Max Precision
    - type: max_recall
      value: 0.9407008086253369
      name: Max Recall
    - type: max_ap
      value: 0.8884154240019244
      name: Max Ap
  - task:
      type: paraphrase-mining
      name: Paraphrase Mining
    dataset:
      name: quora duplicates dev
      type: quora-duplicates-dev
    metrics:
    - type: average_precision
      value: 0.534436244125929
      name: Average Precision
    - type: f1
      value: 0.5447997274541295
      name: F1
    - type: precision
      value: 0.5311002514589362
      name: Precision
    - type: recall
      value: 0.5592246590398161
      name: Recall
    - type: threshold
      value: 0.8626040816307068
      name: Threshold
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: cosine_accuracy@1
      value: 0.928
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9712
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9782
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9874
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.928
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.4151333333333334
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.26656
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.14166
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7993523853760618
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9341884771405065
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9560896250710075
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9766088525134997
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9516150309696244
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9509392857142857
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9390263696194139
      name: Cosine Map@100
    - type: dot_accuracy@1
      value: 0.8926
      name: Dot Accuracy@1
    - type: dot_accuracy@3
      value: 0.9518
      name: Dot Accuracy@3
    - type: dot_accuracy@5
      value: 0.9658
      name: Dot Accuracy@5
    - type: dot_accuracy@10
      value: 0.9768
      name: Dot Accuracy@10
    - type: dot_precision@1
      value: 0.8926
      name: Dot Precision@1
    - type: dot_precision@3
      value: 0.40273333333333333
      name: Dot Precision@3
    - type: dot_precision@5
      value: 0.26076
      name: Dot Precision@5
    - type: dot_precision@10
      value: 0.13882
      name: Dot Precision@10
    - type: dot_recall@1
      value: 0.7679620996617761
      name: Dot Recall@1
    - type: dot_recall@3
      value: 0.9105756956997251
      name: Dot Recall@3
    - type: dot_recall@5
      value: 0.9402185219519044
      name: Dot Recall@5
    - type: dot_recall@10
      value: 0.9623418143294613
      name: Dot Recall@10
    - type: dot_ndcg@10
      value: 0.9263520741106431
      name: Dot Ndcg@10
    - type: dot_mrr@10
      value: 0.9243020634920638
      name: Dot Mrr@10
    - type: dot_map@100
      value: 0.9094019438194247
      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 [sentence-transformers/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 82ad392c08f81be9be9bf065339670b23f2e1493 -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - [sentence-transformers/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("tomaarsen/stsb-distilbert-base-ocl")
# Run inference
sentences = [
    'Is stretching bad?',
    'Is stretching good for you?',
    'If i=0; what will i=i++ do to i?',
]
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>
-->

<!--
### 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

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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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              | 0.86       |
| cosine_accuracy_threshold    | 0.8104     |
| cosine_f1                    | 0.8251     |
| cosine_f1_threshold          | 0.7248     |
| cosine_precision             | 0.7347     |
| cosine_recall                | 0.9407     |
| cosine_ap                    | 0.8872     |
| dot_accuracy                 | 0.828      |
| dot_accuracy_threshold       | 157.3549   |
| dot_f1                       | 0.7899     |
| dot_f1_threshold             | 145.7113   |
| dot_precision                | 0.7155     |
| dot_recall                   | 0.8814     |
| dot_ap                       | 0.8369     |
| manhattan_accuracy           | 0.868      |
| manhattan_accuracy_threshold | 208.0035   |
| manhattan_f1                 | 0.8308     |
| manhattan_f1_threshold       | 208.0035   |
| manhattan_precision          | 0.7922     |
| manhattan_recall             | 0.8733     |
| manhattan_ap                 | 0.8868     |
| euclidean_accuracy           | 0.867      |
| euclidean_accuracy_threshold | 9.2694     |
| euclidean_f1                 | 0.8301     |
| euclidean_f1_threshold       | 9.5257     |
| euclidean_precision          | 0.7888     |
| euclidean_recall             | 0.876      |
| euclidean_ap                 | 0.8884     |
| max_accuracy                 | 0.868      |
| max_accuracy_threshold       | 208.0035   |
| max_f1                       | 0.8308     |
| max_f1_threshold             | 208.0035   |
| max_precision                | 0.7922     |
| max_recall                   | 0.9407     |
| **max_ap**                   | **0.8884** |

#### 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** | **0.5344** |
| f1                    | 0.5448     |
| precision             | 0.5311     |
| recall                | 0.5592     |
| threshold             | 0.8626     |

#### Information Retrieval

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

| Metric              | Value     |
|:--------------------|:----------|
| cosine_accuracy@1   | 0.928     |
| cosine_accuracy@3   | 0.9712    |
| cosine_accuracy@5   | 0.9782    |
| cosine_accuracy@10  | 0.9874    |
| cosine_precision@1  | 0.928     |
| cosine_precision@3  | 0.4151    |
| cosine_precision@5  | 0.2666    |
| cosine_precision@10 | 0.1417    |
| cosine_recall@1     | 0.7994    |
| cosine_recall@3     | 0.9342    |
| cosine_recall@5     | 0.9561    |
| cosine_recall@10    | 0.9766    |
| cosine_ndcg@10      | 0.9516    |
| cosine_mrr@10       | 0.9509    |
| **cosine_map@100**  | **0.939** |
| dot_accuracy@1      | 0.8926    |
| dot_accuracy@3      | 0.9518    |
| dot_accuracy@5      | 0.9658    |
| dot_accuracy@10     | 0.9768    |
| dot_precision@1     | 0.8926    |
| dot_precision@3     | 0.4027    |
| dot_precision@5     | 0.2608    |
| dot_precision@10    | 0.1388    |
| dot_recall@1        | 0.768     |
| dot_recall@3        | 0.9106    |
| dot_recall@5        | 0.9402    |
| dot_recall@10       | 0.9623    |
| dot_ndcg@10         | 0.9264    |
| dot_mrr@10          | 0.9243    |
| dot_map@100         | 0.9094    |

<!--
## 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.*
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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 Dataset

#### sentence-transformers/quora-duplicates

* Dataset: [sentence-transformers/quora-duplicates](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: 15.5 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.46 tokens</li><li>max: 78 tokens</li></ul> | <ul><li>0: ~64.10%</li><li>1: ~35.90%</li></ul> |
* Samples:
  | sentence1                                                                                          | sentence2                                                                         | label          |
  |:---------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------|
  | <code>What are the best ecommerce blogs to do guest posts on about SEO to gain new clients?</code> | <code>Interested in being a guest blogger for an ecommerce marketing blog?</code> | <code>0</code> |
  | <code>How do I learn Informatica online training?</code>                                           | <code>What is Informatica online training?</code>                                 | <code>0</code> |
  | <code>What effects does marijuana use have on the flu?</code>                                      | <code>What effects does Marijuana use have on the common cold?</code>             | <code>0</code> |
* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/losses.html#onlinecontrastiveloss)

### Evaluation Dataset

#### sentence-transformers/quora-duplicates

* Dataset: [sentence-transformers/quora-duplicates](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: 6 tokens</li><li>mean: 15.82 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.91 tokens</li><li>max: 72 tokens</li></ul> | <ul><li>0: ~62.90%</li><li>1: ~37.10%</li></ul> |
* Samples:
  | sentence1                                             | sentence2                                          | label          |
  |:------------------------------------------------------|:---------------------------------------------------|:---------------|
  | <code>How should I prepare for JEE Mains 2017?</code> | <code>How do I prepare for the JEE 2016?</code>    | <code>0</code> |
  | <code>What is the gate exam?</code>                   | <code>What is the GATE exam in engineering?</code> | <code>0</code> |
  | <code>Where do IRS officers get posted?</code>        | <code>Does IRS Officers get posted abroad?</code>  | <code>0</code> |
* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/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`: 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 | loss   | cosine_map@100 | quora-duplicates-dev_average_precision | quora-duplicates_max_ap |
|:------:|:----:|:-------------:|:------:|:--------------:|:--------------------------------------:|:-----------------------:|
| 0      | 0    | -             | -      | 0.9235         | 0.4200                                 | 0.7276                  |
| 0.0640 | 100  | 2.5123        | -      | -              | -                                      | -                       |
| 0.1280 | 200  | 2.0534        | -      | -              | -                                      | -                       |
| 0.1599 | 250  | -             | 1.7914 | 0.9127         | 0.4082                                 | 0.8301                  |
| 0.1919 | 300  | 1.9505        | -      | -              | -                                      | -                       |
| 0.2559 | 400  | 1.9836        | -      | -              | -                                      | -                       |
| 0.3199 | 500  | 1.8462        | 1.5923 | 0.9190         | 0.4445                                 | 0.8688                  |
| 0.3839 | 600  | 1.7734        | -      | -              | -                                      | -                       |
| 0.4479 | 700  | 1.7918        | -      | -              | -                                      | -                       |
| 0.4798 | 750  | -             | 1.5461 | 0.9291         | 0.4943                                 | 0.8707                  |
| 0.5118 | 800  | 1.6157        | -      | -              | -                                      | -                       |
| 0.5758 | 900  | 1.7244        | -      | -              | -                                      | -                       |
| 0.6398 | 1000 | 1.7322        | 1.5294 | 0.9309         | 0.5048                                 | 0.8808                  |
| 0.7038 | 1100 | 1.6825        | -      | -              | -                                      | -                       |
| 0.7678 | 1200 | 1.6823        | -      | -              | -                                      | -                       |
| 0.7997 | 1250 | -             | 1.4812 | 0.9351         | 0.5126                                 | 0.8865                  |
| 0.8317 | 1300 | 1.5707        | -      | -              | -                                      | -                       |
| 0.8957 | 1400 | 1.6145        | -      | -              | -                                      | -                       |
| 0.9597 | 1500 | 1.5795        | 1.4705 | 0.9390         | 0.5344                                 | 0.8884                  |


### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.040 kWh
- **Carbon Emitted**: 0.016 kg of CO2
- **Hours Used**: 0.202 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",
}
```

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