README.md

---
base_model: TaylorAI/bge-micro-v2
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: BGE micro v2 ESG
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 384
      type: dim_384
    metrics:
    - type: cosine_accuracy@1
      value: 0.7549523729242181
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8991823316193206
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9237123830397033
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9447020146674534
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7549523729242181
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2997274438731068
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1847424766079407
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09447020146674537
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.020970899247894956
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.02497728698942558
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.025658677306658433
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.026241722629651493
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.18912117167223944
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8309359566693303
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.023120117824201005
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.7496417432352693
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8958105032453848
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9187389361881481
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9417516648402596
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7496417432352693
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2986035010817949
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1837477872376296
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09417516648402599
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.020823381756535267
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.02488362509014959
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.025520526005226342
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.026159768467784998
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.188171652806899
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8261983036492017
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.022991454812532088
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.7355643597740875
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8874652280198938
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9105622523813538
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9341650509989041
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7355643597740875
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2958217426732979
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1821124504762708
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09341650509989044
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.02043234332705799
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.0246518118894415
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.025293395899482058
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.02594902919441401
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.18580500893220617
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8144083444724101
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.022667974495178208
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.6972098120205682
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8493635673944196
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8830818511337772
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.913175419371154
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6972098120205682
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.28312118913147316
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17661637022675547
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09131754193711542
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.019366939222793565
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.023593432427622775
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.024530051420382712
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.02536598387142095
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.1787893349481174
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7792686076088251
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.021712360244980362
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 32
      type: dim_32
    metrics:
    - type: cosine_accuracy@1
      value: 0.5974036921520695
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.7523392059344179
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.7970159318890668
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8448115990896063
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.5974036921520695
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.25077973531147263
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.15940318637781337
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08448115990896064
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.016594547004224157
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.02089831127595606
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.022139331441362976
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.023466988863600182
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.15933281345013575
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.6849689711507925
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.019142044257794796
      name: Cosine Map@100
---

# BGE micro v2 ESG

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [TaylorAI/bge-micro-v2](https://huggingface.co/TaylorAI/bge-micro-v2). 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:** [TaylorAI/bge-micro-v2](https://huggingface.co/TaylorAI/bge-micro-v2) <!-- at revision 3edf6d7de0faa426b09780416fe61009f26ae589 -->
- **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': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, '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("elsayovita/bge-micro-v2-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>
-->

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### 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_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   | 0.755      |
| cosine_accuracy@3   | 0.8992     |
| cosine_accuracy@5   | 0.9237     |
| cosine_accuracy@10  | 0.9447     |
| cosine_precision@1  | 0.755      |
| cosine_precision@3  | 0.2997     |
| cosine_precision@5  | 0.1847     |
| cosine_precision@10 | 0.0945     |
| cosine_recall@1     | 0.021      |
| cosine_recall@3     | 0.025      |
| cosine_recall@5     | 0.0257     |
| cosine_recall@10    | 0.0262     |
| cosine_ndcg@10      | 0.1891     |
| cosine_mrr@10       | 0.8309     |
| **cosine_map@100**  | **0.0231** |

#### 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   | 0.7496    |
| cosine_accuracy@3   | 0.8958    |
| cosine_accuracy@5   | 0.9187    |
| cosine_accuracy@10  | 0.9418    |
| cosine_precision@1  | 0.7496    |
| cosine_precision@3  | 0.2986    |
| cosine_precision@5  | 0.1837    |
| cosine_precision@10 | 0.0942    |
| cosine_recall@1     | 0.0208    |
| cosine_recall@3     | 0.0249    |
| cosine_recall@5     | 0.0255    |
| cosine_recall@10    | 0.0262    |
| cosine_ndcg@10      | 0.1882    |
| cosine_mrr@10       | 0.8262    |
| **cosine_map@100**  | **0.023** |

#### 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   | 0.7356     |
| cosine_accuracy@3   | 0.8875     |
| cosine_accuracy@5   | 0.9106     |
| cosine_accuracy@10  | 0.9342     |
| cosine_precision@1  | 0.7356     |
| cosine_precision@3  | 0.2958     |
| cosine_precision@5  | 0.1821     |
| cosine_precision@10 | 0.0934     |
| cosine_recall@1     | 0.0204     |
| cosine_recall@3     | 0.0247     |
| cosine_recall@5     | 0.0253     |
| cosine_recall@10    | 0.0259     |
| cosine_ndcg@10      | 0.1858     |
| cosine_mrr@10       | 0.8144     |
| **cosine_map@100**  | **0.0227** |

#### 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   | 0.6972     |
| cosine_accuracy@3   | 0.8494     |
| cosine_accuracy@5   | 0.8831     |
| cosine_accuracy@10  | 0.9132     |
| cosine_precision@1  | 0.6972     |
| cosine_precision@3  | 0.2831     |
| cosine_precision@5  | 0.1766     |
| cosine_precision@10 | 0.0913     |
| cosine_recall@1     | 0.0194     |
| cosine_recall@3     | 0.0236     |
| cosine_recall@5     | 0.0245     |
| cosine_recall@10    | 0.0254     |
| cosine_ndcg@10      | 0.1788     |
| cosine_mrr@10       | 0.7793     |
| **cosine_map@100**  | **0.0217** |

#### 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   | 0.5974     |
| cosine_accuracy@3   | 0.7523     |
| cosine_accuracy@5   | 0.797      |
| cosine_accuracy@10  | 0.8448     |
| cosine_precision@1  | 0.5974     |
| cosine_precision@3  | 0.2508     |
| cosine_precision@5  | 0.1594     |
| cosine_precision@10 | 0.0845     |
| cosine_recall@1     | 0.0166     |
| cosine_recall@3     | 0.0209     |
| cosine_recall@5     | 0.0221     |
| cosine_recall@10    | 0.0235     |
| cosine_ndcg@10      | 0.1593     |
| cosine_mrr@10       | 0.685      |
| **cosine_map@100**  | **0.0191** |

<!--
## 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

#### 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`: False
- `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`: False
- `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     | 5.2501        | -                      | -                      | -                     | -                      | -                     |
| 0.8625     | 20     | 3.4967        | -                      | -                      | -                     | -                      | -                     |
| 1.0350     | 24     | -             | 0.0221                 | 0.0224                 | 0.0185                | 0.0226                 | 0.0210                |
| 1.2264     | 30     | 3.1196        | -                      | -                      | -                     | -                      | -                     |
| 1.6577     | 40     | 2.4428        | -                      | -                      | -                     | -                      | -                     |
| 2.0458     | 49     | -             | 0.0226                 | 0.0229                 | 0.0189                | 0.0230                 | 0.0215                |
| 2.0216     | 50     | 2.2222        | -                      | -                      | -                     | -                      | -                     |
| 2.4528     | 60     | 2.3441        | -                      | -                      | -                     | -                      | -                     |
| 2.8841     | 70     | 2.0096        | -                      | -                      | -                     | -                      | -                     |
| 3.0566     | 74     | -             | 0.0227                 | 0.0230                 | 0.0191                | 0.0231                 | 0.0217                |
| 3.2480     | 80     | 2.3019        | -                      | -                      | -                     | -                      | -                     |
| 3.6792     | 90     | 1.9538        | -                      | -                      | -                     | -                      | -                     |
| **3.7655** | **92** | **-**         | **0.0227**             | **0.023**              | **0.0191**            | **0.0231**             | **0.0217**            |

* 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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