sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:1024 loss:MultipleNegativesRankingLoss arxiv:1908.10084 arxiv:1705.00652 base_model:BAAI/bge-large-en-v1.5 base_model:finetune:BAAI/bge-large-en-v1.5 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: BAAI/bge-large-en-v1.5
library_name: sentence-transformers
metrics:
- 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
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1024
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: After rescue, survivors may require hospital treatment. This must
    be provided as quickly as possible. The SMC should consider having ambulance and
    hospital facilities ready.
  sentences:
  - What should the SMC consider having ready after a rescue?
  - What is critical for mass rescue operations?
  - What can computer programs do to relieve the search planner of computational burden?
- source_sentence: SMCs conduct communication searches when facts are needed to supplement
    initially reported information. Efforts are continued to contact the craft, to
    find out more about a possible distress situation, and to prepare for or to avoid
    a search effort. Section 3.5 has more information on communication searches.MEDICO
    Communications
  sentences:
  - What is generally produced by dead-reckoning navigation alone for search aircraft?
  - What should be the widths of rectangular areas to be covered with a PS pattern
    and the lengths of rectangular areas to be covered with a CS pattern?
  - What is the purpose of SMCs conducting communication searches?
- source_sentence: 'SAR facilities include designated SRUs and other resources which
    can be used to conduct or support SAR operations. An SRU is a unit composed of
    trained personnel and provided with equipment suitable for the expeditious and
    efficient conduct of search and rescue. An SRU can be an air, maritime, or land-based
    facility. Facilities selected as SRUs should be able to reach the scene of distress
    quickly and, in particular, be suitable for one or more of the following operations:–
    providing assistance to prevent or reduce the severity of accidents and the hardship
    of survivors, e.g., escorting an aircraft, standing by a sinking vessel;– conducting
    a search;– delivering supplies and survival equipment to the scene;– rescuing
    survivors;– providing food, medical or other initial needs of survivors; and–
    delivering the survivors to a place of safety. '
  sentences:
  - What are the types of SAR facilities that can be used to conduct or support SAR
    operations?
  - What is the scenario in which a simulated communication search is carried out
    and an air search is planned?
  - What is discussed in detail in various other places in this Manual?
- source_sentence: Support facilities enable the operational response resources (e.g.,
    the RCC and SRUs) to provide the SAR services. Without the supporting resources,
    the operational resources cannot sustain effective operations. There is a wide
    range of support facilities and services, which include the following:Training
    facilities Facility maintenanceCommunications facilities Management functionsNavigation
    systems Research and developmentSAR data providers (SDPs) PlanningMedical facilities
    ExercisesAircraft landing fields Refuelling servicesVoluntary services (e.g.,
    Red Cross) Critical incident stress counsellors Computer resources
  sentences:
  - How many ways are there to train SAR specialists and teams?
  - What types of support facilities are mentioned in the context?
  - What is the duration of a prolonged blast?
- source_sentence: 'Sound funding decisions arise out of accurate assessments made
    of the SAR system. To measure the performance or effectiveness of a SAR system
    usually requires collecting information or statistics and establishing agreed-upon
    goals. All pertinent information should be collected, including where the system
    failed to perform as it should have; failures and successes provide valuable information
    in assessing effectiveness and determining means to improve. '
  sentences:
  - What is required to measure the performance or effectiveness of a SAR system?
  - What is the purpose of having an SRR?
  - What is the effect of decreasing track spacing on the area that can be searched?
model-index:
- name: SentenceTransformer based on BAAI/bge-large-en-v1.5
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.7719298245614035
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9298245614035088
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.956140350877193
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 1.0
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7719298245614035
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3099415204678363
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1912280701754386
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.1
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7719298245614035
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9298245614035088
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.956140350877193
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 1.0
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8884520476480379
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8524470899470901
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.85244708994709
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.7543859649122807
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9122807017543859
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.956140350877193
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9912280701754386
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7543859649122807
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.304093567251462
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1912280701754386
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09912280701754386
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7543859649122807
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9122807017543859
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.956140350877193
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9912280701754386
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8791120820747885
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8425438596491228
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8431704260651629
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.7456140350877193
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8947368421052632
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9385964912280702
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9649122807017544
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7456140350877193
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2982456140350877
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.18771929824561406
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09649122807017543
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7456140350877193
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8947368421052632
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9385964912280702
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9649122807017544
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8623224236283672
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8287628794207742
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8310819942011893
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.7017543859649122
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8245614035087719
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8771929824561403
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9385964912280702
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7017543859649122
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.27485380116959063
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17543859649122803
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09385964912280703
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7017543859649122
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8245614035087719
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8771929824561403
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9385964912280702
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8146917044508328
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7757031467557786
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7788889950899075
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.6228070175438597
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.7543859649122807
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.7894736842105263
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8596491228070176
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6228070175438597
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.25146198830409355
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.15789473684210523
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08596491228070174
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6228070175438597
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.7543859649122807
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.7894736842105263
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8596491228070176
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7406737402395112
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.703104984683932
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.71092932980045
      name: Cosine Map@100
---

# SentenceTransformer based on BAAI/bge-large-en-v1.5

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) on the json dataset. It maps sentences & paragraphs to a 1024-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:** [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) <!-- at revision d4aa6901d3a41ba39fb536a557fa166f842b0e09 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - json
<!-- - **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': 512, 'do_lower_case': True}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)
```

## 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("tessimago/bge-large-repmus-cross_entropy")
# Run inference
sentences = [
    'Sound funding decisions arise out of accurate assessments made of the SAR system. To measure the performance or effectiveness of a SAR system usually requires collecting information or statistics and establishing agreed-upon goals. All pertinent information should be collected, including where the system failed to perform as it should have; failures and successes provide valuable information in assessing effectiveness and determining means to improve. ',
    'What is required to measure the performance or effectiveness of a SAR system?',
    'What is the effect of decreasing track spacing on the area that can be searched?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

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

<!--
### Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details>
-->

<!--
### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

</details>
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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## Evaluation

### Metrics

#### Information Retrieval
* Dataset: `dim_768`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.7719     |
| cosine_accuracy@3   | 0.9298     |
| cosine_accuracy@5   | 0.9561     |
| cosine_accuracy@10  | 1.0        |
| cosine_precision@1  | 0.7719     |
| cosine_precision@3  | 0.3099     |
| cosine_precision@5  | 0.1912     |
| cosine_precision@10 | 0.1        |
| cosine_recall@1     | 0.7719     |
| cosine_recall@3     | 0.9298     |
| cosine_recall@5     | 0.9561     |
| cosine_recall@10    | 1.0        |
| cosine_ndcg@10      | 0.8885     |
| cosine_mrr@10       | 0.8524     |
| **cosine_map@100**  | **0.8524** |

#### Information Retrieval
* Dataset: `dim_512`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.7544     |
| cosine_accuracy@3   | 0.9123     |
| cosine_accuracy@5   | 0.9561     |
| cosine_accuracy@10  | 0.9912     |
| cosine_precision@1  | 0.7544     |
| cosine_precision@3  | 0.3041     |
| cosine_precision@5  | 0.1912     |
| cosine_precision@10 | 0.0991     |
| cosine_recall@1     | 0.7544     |
| cosine_recall@3     | 0.9123     |
| cosine_recall@5     | 0.9561     |
| cosine_recall@10    | 0.9912     |
| cosine_ndcg@10      | 0.8791     |
| cosine_mrr@10       | 0.8425     |
| **cosine_map@100**  | **0.8432** |

#### Information Retrieval
* Dataset: `dim_256`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.7456     |
| cosine_accuracy@3   | 0.8947     |
| cosine_accuracy@5   | 0.9386     |
| cosine_accuracy@10  | 0.9649     |
| cosine_precision@1  | 0.7456     |
| cosine_precision@3  | 0.2982     |
| cosine_precision@5  | 0.1877     |
| cosine_precision@10 | 0.0965     |
| cosine_recall@1     | 0.7456     |
| cosine_recall@3     | 0.8947     |
| cosine_recall@5     | 0.9386     |
| cosine_recall@10    | 0.9649     |
| cosine_ndcg@10      | 0.8623     |
| cosine_mrr@10       | 0.8288     |
| **cosine_map@100**  | **0.8311** |

#### Information Retrieval
* Dataset: `dim_128`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.7018     |
| cosine_accuracy@3   | 0.8246     |
| cosine_accuracy@5   | 0.8772     |
| cosine_accuracy@10  | 0.9386     |
| cosine_precision@1  | 0.7018     |
| cosine_precision@3  | 0.2749     |
| cosine_precision@5  | 0.1754     |
| cosine_precision@10 | 0.0939     |
| cosine_recall@1     | 0.7018     |
| cosine_recall@3     | 0.8246     |
| cosine_recall@5     | 0.8772     |
| cosine_recall@10    | 0.9386     |
| cosine_ndcg@10      | 0.8147     |
| cosine_mrr@10       | 0.7757     |
| **cosine_map@100**  | **0.7789** |

#### Information Retrieval
* Dataset: `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.6228     |
| cosine_accuracy@3   | 0.7544     |
| cosine_accuracy@5   | 0.7895     |
| cosine_accuracy@10  | 0.8596     |
| cosine_precision@1  | 0.6228     |
| cosine_precision@3  | 0.2515     |
| cosine_precision@5  | 0.1579     |
| cosine_precision@10 | 0.086      |
| cosine_recall@1     | 0.6228     |
| cosine_recall@3     | 0.7544     |
| cosine_recall@5     | 0.7895     |
| cosine_recall@10    | 0.8596     |
| cosine_ndcg@10      | 0.7407     |
| cosine_mrr@10       | 0.7031     |
| **cosine_map@100**  | **0.7109** |

<!--
## 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

#### json

* Dataset: json
* Size: 1,024 training samples
* Columns: <code>positive</code> and <code>anchor</code>
* Approximate statistics based on the first 1000 samples:
  |         | positive                                                                             | anchor                                                                           |
  |:--------|:-------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                           |
  | details | <ul><li>min: 10 tokens</li><li>mean: 133.58 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 17.7 tokens</li><li>max: 39 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     | anchor                                                                                                                                 |
  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------|
  | <code>The debriefing helps to ensure that all survivors are rescued, to attend to the physical welfare of each survivor, and to obtain information which may assist and improve SAR services. Proper debriefing techniques include:– due care to avoid worsening a survivor’s condition by excessive debriefing;– careful assessment of the survivor’s statements if the survivor is frightened or excited;– use of a calm voice in questioning;– avoidance of suggesting the answers when obtaining facts; and– explaining that the information requested is important for the success of the SAR operation, and possibly for future SAR operations.</code> | <code>What are some proper debriefing techniques used in SAR services?</code>                                                          |
  | <code>Communicating with passengers is more difficult in remote areas where phone service may be inadequate or lacking. If phones do exist, calling the airline or shipping company may be the best way to check in and find out information. In more populated areas, local agencies may have an emergency evacuation plan or other useful plan that can be implemented.IE961E.indb 21 6/28/2013 10:29:55 AM</code>                                                                                                                                                                                                                                         | <code>What is a good way to check in and find out information in remote areas where phone service may be inadequate or lacking?</code> |
  | <code>Voice communication is the basis of telemedical advice. It allows free dialogue and contributes to the human relationship, which is crucial to any medical consultation. Text messages are a useful complement to the voice telemedical advice and add the reliability of writing. Facsimile allows the exchange of pictures or diagrams, which help to identify a symptom, describe a lesion or the method of treatment. Digital data transmissions (photographs or electrocardiogram) provide an objective and potentially crucial addition to descriptive and subjective clinical data.</code>                                                      | <code>What are the types of communication methods used in telemedical advice?</code>                                                   |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: epoch
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `eval_accumulation_steps`: None
- `learning_rate`: 2e-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`: 4
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `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`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: True
- `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`: True
- `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_fused
- `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
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch   | Step  | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 |
|:-------:|:-----:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 1.0     | 2     | 0.7770                 | 0.8173                 | 0.8316                 | 0.6838                | 0.8448                 |
| **2.0** | **4** | **0.7858**             | **0.8221**             | **0.8326**             | **0.6993**            | **0.8478**             |
| 3.0     | 6     | 0.7801                 | 0.8297                 | 0.8412                 | 0.7101                | 0.8517                 |
| 4.0     | 8     | 0.7789                 | 0.8311                 | 0.8432                 | 0.7109                | 0.8524                 |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.1.0
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.34.2
- Datasets: 2.19.1
- 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}
}
```

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