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---
language:
- en
license: apache-2.0
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:161
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-base-en-v1.5
datasets: []
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
widget:
- source_sentence: 'As per Part II of the PDPA, Personal Data Protection Commission
(PDPC) is the
regulatory body to enforce the provisions of PDPA. The PDPC is empowered with
broad discretion to issue remedial directions, initiate investigation
inquiries, and impose fines and penalties on the organisations in case of any
non-compliance of PDPA.
1
If organisations misuse the personal data or hide information concerning its
collection, use, or disclosure, PDPA states penalties not exceeding **S$50,000
(approx. $36,000)**.
2
Penalty for hindering a PDPC investigation can lead to a fine of not more than
**S$100,000 (approx. $72,000)**. The PDPA states that companies are also
liable for their employees’ actions, whether they are aware of them or not.
3
New amendments to PDPA have enforced increased financial penalties for
breaches of the PDPA up to **10%** of annual gross turnover in Singapore, or
**S$ 1 million** , whichever is higher.
4
Non-compliance with specific provisions under the PDPA may also constitute an
offense, for which a fine or a term of **imprisonment** may be imposed.
5
An individual can bring a private civil action against an organisation for
having suffered **loss or damage** directly due to a contravention of the
provisions of the PDPA.'
sentences:
- What is the right to notice under the CCPA?
- What are the risks of non-compliance with the PDPA?
- What is the definition of personal data under the PDP Law?
- source_sentence: The DPA requires all data controllers to take appropriate technical
and organisational measures that are necessary to protect data from unauthorised
destruction, negligent loss, unauthorised alteration or access and any other unauthorised
processing of the data.
sentences:
- Which regulatory authority enforces GDPR in France?
- What are the security requirements under the DPA?
- How do PIPEDA and GDPR differ?
- source_sentence: if the data controller or the data processor holds a valid registration
certificate authorizing him or her to store personal data outside Rwanda
sentences:
- What is the difference between GDPR and a Data Protection Act?
- What is the voluntary certification by the CPPA?
- Where is personal data storage outside of Rwanda permitted?
- source_sentence: The PDP law will regulate sensitive personal data as well as other
personal data that may endanger or harm the privacy of the data subject.
sentences:
- What is the material scope of the PDP Law?
- What is the definition of personal information under the DPA in the Philippines?
- What does Securiti offer to help with data privacy compliance?
- source_sentence: Thailand's PDPA applies to any legal entity collecting, using,
or disclosing a natural (and alive) person's personal data.
sentences:
- Who does the Thailand's PDPA apply to?
- What penalties could an organization face for infringing Kenya's Data Protection
Act?
- What is the CPRA?
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on BAAI/bge-base-en-v1.5
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.5
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8333333333333334
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9444444444444444
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 1.0
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.5
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.27777777777777773
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1888888888888889
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.10000000000000002
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.5
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8333333333333334
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9444444444444444
name: Cosine Recall@5
- type: cosine_recall@10
value: 1.0
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.736082728585743
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6515432098765431
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6515432098765432
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.5
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7777777777777778
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9444444444444444
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 1.0
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.5
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.25925925925925924
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1888888888888889
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.10000000000000002
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.5
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7777777777777778
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9444444444444444
name: Cosine Recall@5
- type: cosine_recall@10
value: 1.0
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.744344523828935
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6626543209876543
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6626543209876543
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.5
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8888888888888888
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8888888888888888
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 1.0
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.5
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2962962962962962
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1777777777777778
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.10000000000000002
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.5
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8888888888888888
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8888888888888888
name: Cosine Recall@5
- type: cosine_recall@10
value: 1.0
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7569877225340996
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6790123456790123
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6790123456790124
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.5
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8333333333333334
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8888888888888888
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9444444444444444
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.5
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.27777777777777773
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1777777777777778
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09444444444444446
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.5
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8333333333333334
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8888888888888888
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9444444444444444
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7291386563584304
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6589506172839507
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6604938271604938
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.4444444444444444
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.6111111111111112
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.6666666666666666
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 1.0
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.4444444444444444
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2037037037037037
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.13333333333333336
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.10000000000000002
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.4444444444444444
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.6111111111111112
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.6666666666666666
name: Cosine Recall@5
- type: cosine_recall@10
value: 1.0
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.6740519326169271
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.5768298059964727
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.5768298059964727
name: Cosine Map@100
---
# SentenceTransformer based on BAAI/bge-base-en-v1.5
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5). It maps sentences & paragraphs to a 768-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-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 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': 768, '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("MugheesAwan11/bge-base-securiti-dataset-1-v5")
# Run inference
sentences = [
"Thailand's PDPA applies to any legal entity collecting, using, or disclosing a natural (and alive) person's personal data.",
"Who does the Thailand's PDPA apply to?",
"What penalties could an organization face for infringing Kenya's Data Protection Act?",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# 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_768`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.5 |
| cosine_accuracy@3 | 0.8333 |
| cosine_accuracy@5 | 0.9444 |
| cosine_accuracy@10 | 1.0 |
| cosine_precision@1 | 0.5 |
| cosine_precision@3 | 0.2778 |
| cosine_precision@5 | 0.1889 |
| cosine_precision@10 | 0.1 |
| cosine_recall@1 | 0.5 |
| cosine_recall@3 | 0.8333 |
| cosine_recall@5 | 0.9444 |
| cosine_recall@10 | 1.0 |
| cosine_ndcg@10 | 0.7361 |
| cosine_mrr@10 | 0.6515 |
| **cosine_map@100** | **0.6515** |
#### Information Retrieval
* Dataset: `dim_512`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.5 |
| cosine_accuracy@3 | 0.7778 |
| cosine_accuracy@5 | 0.9444 |
| cosine_accuracy@10 | 1.0 |
| cosine_precision@1 | 0.5 |
| cosine_precision@3 | 0.2593 |
| cosine_precision@5 | 0.1889 |
| cosine_precision@10 | 0.1 |
| cosine_recall@1 | 0.5 |
| cosine_recall@3 | 0.7778 |
| cosine_recall@5 | 0.9444 |
| cosine_recall@10 | 1.0 |
| cosine_ndcg@10 | 0.7443 |
| cosine_mrr@10 | 0.6627 |
| **cosine_map@100** | **0.6627** |
#### 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 | 0.5 |
| cosine_accuracy@3 | 0.8889 |
| cosine_accuracy@5 | 0.8889 |
| cosine_accuracy@10 | 1.0 |
| cosine_precision@1 | 0.5 |
| cosine_precision@3 | 0.2963 |
| cosine_precision@5 | 0.1778 |
| cosine_precision@10 | 0.1 |
| cosine_recall@1 | 0.5 |
| cosine_recall@3 | 0.8889 |
| cosine_recall@5 | 0.8889 |
| cosine_recall@10 | 1.0 |
| cosine_ndcg@10 | 0.757 |
| cosine_mrr@10 | 0.679 |
| **cosine_map@100** | **0.679** |
#### 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 | 0.5 |
| cosine_accuracy@3 | 0.8333 |
| cosine_accuracy@5 | 0.8889 |
| cosine_accuracy@10 | 0.9444 |
| cosine_precision@1 | 0.5 |
| cosine_precision@3 | 0.2778 |
| cosine_precision@5 | 0.1778 |
| cosine_precision@10 | 0.0944 |
| cosine_recall@1 | 0.5 |
| cosine_recall@3 | 0.8333 |
| cosine_recall@5 | 0.8889 |
| cosine_recall@10 | 0.9444 |
| cosine_ndcg@10 | 0.7291 |
| cosine_mrr@10 | 0.659 |
| **cosine_map@100** | **0.6605** |
#### 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 | 0.4444 |
| cosine_accuracy@3 | 0.6111 |
| cosine_accuracy@5 | 0.6667 |
| cosine_accuracy@10 | 1.0 |
| cosine_precision@1 | 0.4444 |
| cosine_precision@3 | 0.2037 |
| cosine_precision@5 | 0.1333 |
| cosine_precision@10 | 0.1 |
| cosine_recall@1 | 0.4444 |
| cosine_recall@3 | 0.6111 |
| cosine_recall@5 | 0.6667 |
| cosine_recall@10 | 1.0 |
| cosine_ndcg@10 | 0.6741 |
| cosine_mrr@10 | 0.5768 |
| **cosine_map@100** | **0.5768** |
<!--
## 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: 161 training samples
* Columns: <code>positive</code> and <code>anchor</code>
* Approximate statistics based on the first 1000 samples:
| | positive | anchor |
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 5 tokens</li><li>mean: 40.09 tokens</li><li>max: 481 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 13.01 tokens</li><li>max: 24 tokens</li></ul> |
* Samples:
| positive | anchor |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------|
| <code>The DPA may impose administrative fines of up to €10 million, or up to 2%<br>of<br>worldwide turnover. The DPA may also impose heavier fines up to €20 million,<br>or up to 4% of worldwide turnover.</code> | <code>What is the penalty for non-compliance with the GDPR in Italy?</code> |
| <code>As per the DPA, the data handler must seek consent in writing from the data subject to collect any sensitive personal data.</code> | <code>What are the consent requirements under the DPA?</code> |
| <code>China's cybersecurity laws include the Cybersecurity Law, which governs<br>various aspects of cybersecurity, data protection, and the obligations of<br>organizations to ensure the security of networks and data within China's<br>territory.</code> | <code>What are the cybersecurity laws in China?</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64
],
"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`: 2
- `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`: 2
- `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
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 |
|:-------:|:-----:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 1.0 | 3 | 0.6510 | 0.6691 | 0.6534 | 0.5641 | 0.6515 |
| **2.0** | **6** | **0.6605** | **0.679** | **0.6627** | **0.5768** | **0.6515** |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.31.0
- Datasets: 2.19.1
- 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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