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---
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: 0.7393576666947652
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8871280451825002
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9143555593020315
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9382955407569755
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7393576666947652
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2957093483941667
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1828711118604063
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09382955407569755
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.020537712963743484
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.024642445699513908
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.02539876553616755
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.026063765021027103
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.18655528566337626
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8176322873975245
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.022756262897092067
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.731602461434713
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8831661468431257
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9111523223467926
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9355137823484785
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.731602461434713
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2943887156143752
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.18223046446935853
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09355137823484787
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.020322290595408698
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.024532392967864608
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.02530978673185536
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.02598649395412441
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.1854736961250685
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8120234114607371
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.022602117473168613
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.7171035994267891
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8735564359774087
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9012897243530305
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.927927168507123
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7171035994267891
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2911854786591362
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1802579448706061
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09279271685071232
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.019919544428521924
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.02426545655492803
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.025035825676473073
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.025775754680753424
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.18301753980732727
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7997301868287288
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.022264162086570314
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.6758829975554245
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8359605496080249
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8713647475343504
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9060945797858889
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6758829975554245
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2786535165360083
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1742729495068701
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0906094579785889
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.018774527709872903
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.0232211263780007
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.024204576320398637
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.025169293882941365
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.17554680827328792
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7621402212294056
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.02123787521914149
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 32
type: dim_32
metrics:
- type: cosine_accuracy@1
value: 0.575908286268229
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7347214026806036
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.780156790019388
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8298069628255922
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.575908286268229
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.24490713422686783
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1560313580038776
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08298069628255922
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.015997452396339696
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.020408927852238995
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.021671021944983007
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.02305019341182201
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.1551668722356578
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6648409286443452
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.01858718928494409
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")
# 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 | 0.7394 |
| cosine_accuracy@3 | 0.8871 |
| cosine_accuracy@5 | 0.9144 |
| cosine_accuracy@10 | 0.9383 |
| cosine_precision@1 | 0.7394 |
| cosine_precision@3 | 0.2957 |
| cosine_precision@5 | 0.1829 |
| cosine_precision@10 | 0.0938 |
| cosine_recall@1 | 0.0205 |
| cosine_recall@3 | 0.0246 |
| cosine_recall@5 | 0.0254 |
| cosine_recall@10 | 0.0261 |
| cosine_ndcg@10 | 0.1866 |
| cosine_mrr@10 | 0.8176 |
| **cosine_map@100** | **0.0228** |
#### 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 | 0.7316 |
| cosine_accuracy@3 | 0.8832 |
| cosine_accuracy@5 | 0.9112 |
| cosine_accuracy@10 | 0.9355 |
| cosine_precision@1 | 0.7316 |
| cosine_precision@3 | 0.2944 |
| cosine_precision@5 | 0.1822 |
| cosine_precision@10 | 0.0936 |
| cosine_recall@1 | 0.0203 |
| cosine_recall@3 | 0.0245 |
| cosine_recall@5 | 0.0253 |
| cosine_recall@10 | 0.026 |
| cosine_ndcg@10 | 0.1855 |
| cosine_mrr@10 | 0.812 |
| **cosine_map@100** | **0.0226** |
#### 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 | 0.7171 |
| cosine_accuracy@3 | 0.8736 |
| cosine_accuracy@5 | 0.9013 |
| cosine_accuracy@10 | 0.9279 |
| cosine_precision@1 | 0.7171 |
| cosine_precision@3 | 0.2912 |
| cosine_precision@5 | 0.1803 |
| cosine_precision@10 | 0.0928 |
| cosine_recall@1 | 0.0199 |
| cosine_recall@3 | 0.0243 |
| cosine_recall@5 | 0.025 |
| cosine_recall@10 | 0.0258 |
| cosine_ndcg@10 | 0.183 |
| cosine_mrr@10 | 0.7997 |
| **cosine_map@100** | **0.0223** |
#### 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 | 0.6759 |
| cosine_accuracy@3 | 0.836 |
| cosine_accuracy@5 | 0.8714 |
| cosine_accuracy@10 | 0.9061 |
| cosine_precision@1 | 0.6759 |
| cosine_precision@3 | 0.2787 |
| cosine_precision@5 | 0.1743 |
| cosine_precision@10 | 0.0906 |
| cosine_recall@1 | 0.0188 |
| cosine_recall@3 | 0.0232 |
| cosine_recall@5 | 0.0242 |
| cosine_recall@10 | 0.0252 |
| cosine_ndcg@10 | 0.1755 |
| cosine_mrr@10 | 0.7621 |
| **cosine_map@100** | **0.0212** |
#### 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 | 0.5759 |
| cosine_accuracy@3 | 0.7347 |
| cosine_accuracy@5 | 0.7802 |
| cosine_accuracy@10 | 0.8298 |
| cosine_precision@1 | 0.5759 |
| cosine_precision@3 | 0.2449 |
| cosine_precision@5 | 0.156 |
| cosine_precision@10 | 0.083 |
| cosine_recall@1 | 0.016 |
| cosine_recall@3 | 0.0204 |
| cosine_recall@5 | 0.0217 |
| cosine_recall@10 | 0.0231 |
| cosine_ndcg@10 | 0.1552 |
| cosine_mrr@10 | 0.6648 |
| **cosine_map@100** | **0.0186** |
<!--
## 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.*
-->
<!--
### 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`: 2
- `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`: 2
- `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 | 5.0772 | - | - | - | - | - |
| 0.8625 | 20 | 3.2666 | - | - | - | - | - |
| 1.0350 | 24 | - | 0.0221 | 0.0224 | 0.0185 | 0.0226 | 0.0211 |
| 1.2264 | 30 | 3.1157 | - | - | - | - | - |
| 1.6577 | 40 | 2.585 | - | - | - | - | - |
| **1.9164** | **46** | **-** | **0.0223** | **0.0226** | **0.0186** | **0.0228** | **0.0212** |
* 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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