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bge-small-en-v1.5-esg-v2

Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:11863
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results
text-embeddings-inference
Inference Endpoints
Model card Files Community
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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: 0.786984742476608
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.*
-->
<!--
### 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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