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---
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: 0.7661637022675546
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9170530220011801
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9370311051167496
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9542274298238219
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7661637022675546
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.30568434066706
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.18740622102334994
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09542274298238222
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.021282325062987634
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.025473695055588344
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.026028641808798603
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.026506317495106176
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.19177581579273692
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.843606136995247
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.023463069757038203
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.7621175082188316
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9118266880215797
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9353451909297816
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9527944027648992
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7621175082188316
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.3039422293405265
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.18706903818595635
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09527944027648994
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.02116993078385644
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.025328519111710558
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.025981810859160608
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.026466511187913874
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.19114210787645763
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8402866254821924
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.023374206451884923
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.7469442805361207
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.898423670235185
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9232066087836129
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9444491275394082
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7469442805361207
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2994745567450616
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1846413217567226
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09444491275394083
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.020748452237114468
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.02495621306208848
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.025644628021767035
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.02623469798720579
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.1883811701569402
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8264706590720244
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.02300099952981619
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.7106128298069628
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8668970749388856
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8978336002697462
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9243867487144904
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7106128298069628
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.28896569164629515
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17956672005394925
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09243867487144905
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.01973924527241564
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.02408047430385794
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.02493982222971518
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.02567740968651363
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.1818069773338387
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7936283816963235
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.022106633007589808
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 32
type: dim_32
metrics:
- type: cosine_accuracy@1
value: 0.6166231138835033
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7788923543791622
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8194385905757396
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8608277838658013
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6166231138835033
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.259630784793054
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16388771811514793
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08608277838658013
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.017128419830097316
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.02163589873275451
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.022762183071548335
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.02391188288516115
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.16371507022328244
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7058398528705336
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.019714839230632157
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")
# 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 | 0.7662 |
| cosine_accuracy@3 | 0.9171 |
| cosine_accuracy@5 | 0.937 |
| cosine_accuracy@10 | 0.9542 |
| cosine_precision@1 | 0.7662 |
| cosine_precision@3 | 0.3057 |
| cosine_precision@5 | 0.1874 |
| cosine_precision@10 | 0.0954 |
| cosine_recall@1 | 0.0213 |
| cosine_recall@3 | 0.0255 |
| cosine_recall@5 | 0.026 |
| cosine_recall@10 | 0.0265 |
| cosine_ndcg@10 | 0.1918 |
| cosine_mrr@10 | 0.8436 |
| **cosine_map@100** | **0.0235** |
#### 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 | 0.7621 |
| cosine_accuracy@3 | 0.9118 |
| cosine_accuracy@5 | 0.9353 |
| cosine_accuracy@10 | 0.9528 |
| cosine_precision@1 | 0.7621 |
| cosine_precision@3 | 0.3039 |
| cosine_precision@5 | 0.1871 |
| cosine_precision@10 | 0.0953 |
| cosine_recall@1 | 0.0212 |
| cosine_recall@3 | 0.0253 |
| cosine_recall@5 | 0.026 |
| cosine_recall@10 | 0.0265 |
| cosine_ndcg@10 | 0.1911 |
| cosine_mrr@10 | 0.8403 |
| **cosine_map@100** | **0.0234** |
#### 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 | 0.7469 |
| cosine_accuracy@3 | 0.8984 |
| cosine_accuracy@5 | 0.9232 |
| cosine_accuracy@10 | 0.9444 |
| cosine_precision@1 | 0.7469 |
| cosine_precision@3 | 0.2995 |
| cosine_precision@5 | 0.1846 |
| cosine_precision@10 | 0.0944 |
| cosine_recall@1 | 0.0207 |
| cosine_recall@3 | 0.025 |
| cosine_recall@5 | 0.0256 |
| cosine_recall@10 | 0.0262 |
| cosine_ndcg@10 | 0.1884 |
| cosine_mrr@10 | 0.8265 |
| **cosine_map@100** | **0.023** |
#### 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 | 0.7106 |
| cosine_accuracy@3 | 0.8669 |
| cosine_accuracy@5 | 0.8978 |
| cosine_accuracy@10 | 0.9244 |
| cosine_precision@1 | 0.7106 |
| cosine_precision@3 | 0.289 |
| cosine_precision@5 | 0.1796 |
| cosine_precision@10 | 0.0924 |
| cosine_recall@1 | 0.0197 |
| cosine_recall@3 | 0.0241 |
| cosine_recall@5 | 0.0249 |
| cosine_recall@10 | 0.0257 |
| cosine_ndcg@10 | 0.1818 |
| cosine_mrr@10 | 0.7936 |
| **cosine_map@100** | **0.0221** |
#### 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 | 0.6166 |
| cosine_accuracy@3 | 0.7789 |
| cosine_accuracy@5 | 0.8194 |
| cosine_accuracy@10 | 0.8608 |
| cosine_precision@1 | 0.6166 |
| cosine_precision@3 | 0.2596 |
| cosine_precision@5 | 0.1639 |
| cosine_precision@10 | 0.0861 |
| cosine_recall@1 | 0.0171 |
| cosine_recall@3 | 0.0216 |
| cosine_recall@5 | 0.0228 |
| cosine_recall@10 | 0.0239 |
| cosine_ndcg@10 | 0.1637 |
| cosine_mrr@10 | 0.7058 |
| **cosine_map@100** | **0.0197** |
<!--
## 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`: 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`: 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`: 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** | **-** | **0.023** | **0.0234** | **0.0197** | **0.0235** | **0.0221** |
* 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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