sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:11863 loss:MatryoshkaLoss loss:MultipleNegativesRankingLoss en arxiv:1908.10084 arxiv:2205.13147 arxiv:1705.00652 base_model:BAAI/bge-small-en-v1.5 base_model:finetune:BAAI/bge-small-en-v1.5 license:apache-2.0 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: BAAI/bge-small-en-v1.5
datasets: []
language:
- en
library_name: sentence-transformers
license: apache-2.0
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:11863
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: In the fiscal year 2022, the emissions were categorized into different
    scopes, with each scope representing a specific source of emissions
  sentences:
  - 'Question: What is NetLink proactive in identifying to be more efficient in? '
  - What standard is the Environment, Health, and Safety Management System (EHSMS)
    audited to by a third-party accredited certification body at the operational assets
    level of CLI?
  - What do the different scopes represent in terms of emissions in the fiscal year
    2022?
- source_sentence: NetLink is committed to protecting the security of all information
    and information systems, including both end-user data and corporate data. To this
    end, management ensures that the appropriate IT policies, personal data protection
    policy, risk mitigation strategies, cyber security programmes, systems, processes,
    and controls are in place to protect our IT systems and confidential data
  sentences:
  - '"What recognition did NetLink receive in FY22?"'
  - What measures does NetLink have in place to protect the security of all information
    and information systems, including end-user data and corporate data?
  - 'Question: What does Disclosure 102-10 discuss regarding the organization and
    its supply chain?'
- source_sentence: In the domain of economic performance, the focus is on the financial
    health and growth of the organization, ensuring sustainable profitability and
    value creation for stakeholders
  sentences:
  - What does NetLink prioritize by investing in its network to ensure reliability
    and quality of infrastructure?
  - What percentage of the total energy was accounted for by heat, steam, and chilled
    water in 2021 according to the given information?
  - What is the focus in the domain of economic performance, ensuring sustainable
    profitability and value creation for stakeholders?
- source_sentence: Disclosure 102-41 discusses collective bargaining agreements and
    is found on page 98
  sentences:
  - What topic is discussed in Disclosure 102-41 on page 98 of the document?
  - What was the number of cases in 2021, following a decrease from 42 cases in 2020?
  - What type of data does GRI 101 provide in relation to connecting the nation?
- source_sentence: Employee health and well-being has never been more topical than
    it was in the past year. We understand that people around the world, including
    our employees, have been increasingly exposed to factors affecting their physical
    and mental wellbeing. We are committed to creating an environment that supports
    our employees and ensures they feel valued and have a sense of belonging. We utilised
  sentences:
  - What aspect of the standard covers the evaluation of the management approach?
  - 'Question: What is the company''s commitment towards its employees'' health and
    well-being based on the provided context information?'
  - What types of skills does NetLink focus on developing through their training and
    development opportunities for employees?
model-index:
- name: BAAI BGE small en v1.5 ESG
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 384
      type: dim_384
    metrics:
    - type: cosine_accuracy@1
      value: 0.786984742476608
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9269156199949422
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.944617718958105
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9597066509314676
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.786984742476608
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3089718733316474
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.18892354379162102
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09597066509314678
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.021860687291016895
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.025747656110970626
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.026239381082169593
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.026658518081429664
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.19459455903970813
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8588156921146056
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.023886995279989515
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.7815055213689623
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9236280873303548
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9421731433870016
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9596223552221191
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7815055213689623
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.30787602911011824
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.18843462867740032
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09596223552221193
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.021708486704693403
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.025656335759176533
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.026171476205194496
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.02665617653394776
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.19396598426779785
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8550811914864019
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.023784308256522512
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.7713057405378067
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9141869678833348
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9346708252549946
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9532158813116413
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7713057405378067
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3047289892944449
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.18693416505099894
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09532158813116413
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.021425159459383523
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.025394082441203752
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.025963078479305412
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.026478218925323375
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.192049680708846
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8456702445512195
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.023531692780408037
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.7428137907780494
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.892438674871449
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9184860490601029
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9411615948748209
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7428137907780494
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.297479558290483
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1836972098120206
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09411615948748209
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.02063371641050138
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.024789963190873596
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.02551350136278064
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.026143377635411698
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.18745029665008597
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8220114494981732
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.022884160441989647
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 32
      type: dim_32
    metrics:
    - type: cosine_accuracy@1
      value: 0.6668633566551463
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8242434460085981
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8640310208210402
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8987608530725786
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6668633566551463
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.27474781533619935
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17280620416420805
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08987608530725787
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.018523982129309623
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.022895651278016623
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.024000861689473345
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.02496557925201608
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.17367624271978654
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7532998425142056
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.02100792923667254
      name: Cosine Map@100
---

# BAAI BGE small en v1.5 ESG

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5). It maps sentences & paragraphs to a 384-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-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) <!-- at revision 5c38ec7c405ec4b44b94cc5a9bb96e735b38267a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **Language:** en
- **License:** apache-2.0

### 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': 384, '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("elsayovita/bge-small-en-v1.5-esg-v2")
# Run inference
sentences = [
    'Employee health and well-being has never been more topical than it was in the past year. We understand that people around the world, including our employees, have been increasingly exposed to factors affecting their physical and mental wellbeing. We are committed to creating an environment that supports our employees and ensures they feel valued and have a sense of belonging. We utilised',
    "Question: What is the company's commitment towards its employees' health and well-being based on the provided context information?",
    'What types of skills does NetLink focus on developing through their training and development opportunities for employees?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# 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.*
-->

## Evaluation

### Metrics

#### Information Retrieval
* Dataset: `dim_384`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.787      |
| cosine_accuracy@3   | 0.9269     |
| cosine_accuracy@5   | 0.9446     |
| cosine_accuracy@10  | 0.9597     |
| cosine_precision@1  | 0.787      |
| cosine_precision@3  | 0.309      |
| cosine_precision@5  | 0.1889     |
| cosine_precision@10 | 0.096      |
| cosine_recall@1     | 0.0219     |
| cosine_recall@3     | 0.0257     |
| cosine_recall@5     | 0.0262     |
| cosine_recall@10    | 0.0267     |
| cosine_ndcg@10      | 0.1946     |
| cosine_mrr@10       | 0.8588     |
| **cosine_map@100**  | **0.0239** |

#### 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   | 0.7815     |
| cosine_accuracy@3   | 0.9236     |
| cosine_accuracy@5   | 0.9422     |
| cosine_accuracy@10  | 0.9596     |
| cosine_precision@1  | 0.7815     |
| cosine_precision@3  | 0.3079     |
| cosine_precision@5  | 0.1884     |
| cosine_precision@10 | 0.096      |
| cosine_recall@1     | 0.0217     |
| cosine_recall@3     | 0.0257     |
| cosine_recall@5     | 0.0262     |
| cosine_recall@10    | 0.0267     |
| cosine_ndcg@10      | 0.194      |
| cosine_mrr@10       | 0.8551     |
| **cosine_map@100**  | **0.0238** |

#### 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   | 0.7713     |
| cosine_accuracy@3   | 0.9142     |
| cosine_accuracy@5   | 0.9347     |
| cosine_accuracy@10  | 0.9532     |
| cosine_precision@1  | 0.7713     |
| cosine_precision@3  | 0.3047     |
| cosine_precision@5  | 0.1869     |
| cosine_precision@10 | 0.0953     |
| cosine_recall@1     | 0.0214     |
| cosine_recall@3     | 0.0254     |
| cosine_recall@5     | 0.026      |
| cosine_recall@10    | 0.0265     |
| cosine_ndcg@10      | 0.192      |
| cosine_mrr@10       | 0.8457     |
| **cosine_map@100**  | **0.0235** |

#### 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   | 0.7428     |
| cosine_accuracy@3   | 0.8924     |
| cosine_accuracy@5   | 0.9185     |
| cosine_accuracy@10  | 0.9412     |
| cosine_precision@1  | 0.7428     |
| cosine_precision@3  | 0.2975     |
| cosine_precision@5  | 0.1837     |
| cosine_precision@10 | 0.0941     |
| cosine_recall@1     | 0.0206     |
| cosine_recall@3     | 0.0248     |
| cosine_recall@5     | 0.0255     |
| cosine_recall@10    | 0.0261     |
| cosine_ndcg@10      | 0.1875     |
| cosine_mrr@10       | 0.822      |
| **cosine_map@100**  | **0.0229** |

#### Information Retrieval
* Dataset: `dim_32`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value     |
|:--------------------|:----------|
| cosine_accuracy@1   | 0.6669    |
| cosine_accuracy@3   | 0.8242    |
| cosine_accuracy@5   | 0.864     |
| cosine_accuracy@10  | 0.8988    |
| cosine_precision@1  | 0.6669    |
| cosine_precision@3  | 0.2747    |
| cosine_precision@5  | 0.1728    |
| cosine_precision@10 | 0.0899    |
| cosine_recall@1     | 0.0185    |
| cosine_recall@3     | 0.0229    |
| cosine_recall@5     | 0.024     |
| cosine_recall@10    | 0.025     |
| cosine_ndcg@10      | 0.1737    |
| cosine_mrr@10       | 0.7533    |
| **cosine_map@100**  | **0.021** |

<!--
## 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.*
-->

## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 11,863 training samples
* Columns: <code>context</code> and <code>question</code>
* Approximate statistics based on the first 1000 samples:
  |         | context                                                                             | question                                                                          |
  |:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                            |
  | details | <ul><li>min: 13 tokens</li><li>mean: 40.74 tokens</li><li>max: 277 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 24.4 tokens</li><li>max: 62 tokens</li></ul> |
* Samples:
  | context                                                                                                                                                             | question                                                                                                                                                      |
  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>The engagement with key stakeholders involves various topics and methods throughout the year</code>                                                           | <code>Question: What does the engagement with key stakeholders involve throughout the year?</code>                                                            |
  | <code>For unitholders and analysts, the focus is on business and operations, the release of financial results, and the overall performance and announcements</code> | <code>Question: What is the focus for unitholders and analysts in terms of business and operations, financial results, performance, and announcements?</code> |
  | <code>These are communicated through press releases and other required disclosures via SGXNet and NetLink's website</code>                                          | <code>What platform is used to communicate press releases and required disclosures for NetLink?</code>                                                        |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          384,
          256,
          128,
          64,
          32
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

### 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
- `batch_sampler`: no_duplicates

#### 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
- `eval_on_start`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch      | Step   | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_32_cosine_map@100 | dim_384_cosine_map@100 | dim_64_cosine_map@100 |
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|:---------------------:|
| 0.4313     | 10     | 4.3426        | -                      | -                      | -                     | -                      | -                     |
| 0.8625     | 20     | 2.7083        | -                      | -                      | -                     | -                      | -                     |
| 1.0350     | 24     | -             | 0.0229                 | 0.0233                 | 0.0195                | 0.0234                 | 0.0220                |
| 1.2264     | 30     | 2.6835        | -                      | -                      | -                     | -                      | -                     |
| 1.6577     | 40     | 2.1702        | -                      | -                      | -                     | -                      | -                     |
| 1.9164     | 46     | -             | 0.0230                 | 0.0234                 | 0.0197                | 0.0235                 | 0.0221                |
| 0.4313     | 10     | 2.2406        | -                      | -                      | -                     | -                      | -                     |
| 0.8625     | 20     | 1.8606        | -                      | -                      | -                     | -                      | -                     |
| 1.0350     | 24     | -             | 0.0233                 | 0.0236                 | 0.0204                | 0.0237                 | 0.0225                |
| 1.2264     | 30     | 2.0645        | -                      | -                      | -                     | -                      | -                     |
| 1.6577     | 40     | 1.6752        | -                      | -                      | -                     | -                      | -                     |
| 2.0458     | 49     | -             | 0.0235                 | 0.0237                 | 0.0208                | 0.0238                 | 0.0228                |
| 2.0216     | 50     | 1.7855        | -                      | -                      | -                     | -                      | -                     |
| 2.4528     | 60     | 1.7333        | -                      | -                      | -                     | -                      | -                     |
| 2.8841     | 70     | 1.5116        | -                      | -                      | -                     | -                      | -                     |
| 3.0566     | 74     | -             | 0.0235                 | 0.0238                 | 0.0210                | 0.0239                 | 0.0229                |
| 3.2480     | 80     | 1.7812        | -                      | -                      | -                     | -                      | -                     |
| 3.6792     | 90     | 1.4886        | -                      | -                      | -                     | -                      | -                     |
| **3.7655** | **92** | **-**         | **0.0235**             | **0.0238**             | **0.021**             | **0.0239**             | **0.0229**            |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.42.4
- PyTorch: 2.4.0+cu121
- Accelerate: 0.32.1
- Datasets: 2.21.0
- 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",
}
```

#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning}, 
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}
```

#### 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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