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---
base_model: BAAI/bge-base-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:6300
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: A number of factors may impact ESKD growth rates, including mortality
rates for dialysis patients or CKD patients, the aging of the U.S. population,
transplant rates, incidence rates for diseases that cause kidney failure such
as diabetes and hypertension, growth rates of minority populations with higher
than average incidence rates of ESKD.
sentences:
- By how much did the company increase its quarterly cash dividend in February 2023?
- What factors may impact the growth rates of the ESKD patient population?
- What percentage increase did salaries and related costs experience at Delta Air
Lines from 2022 to 2023?
- source_sentence: HIV product sales increased 6% to $18.2 billion in 2023, compared
to 2022.
sentences:
- What were the present values of lease liabilities for operating and finance leases
as of December 31, 2023?
- By what percentage did HIV product sales increase in 2023 compared to the previous
year?
- How is interest income not attributable to the Card Member loan portfolio primarily
represented in financial documents?
- source_sentence: If a violation is found, a broad range of remedies is potentially
available to the Commission and/or CMA, including imposing a fine and/or the prohibition
or restriction of certain business practices.
sentences:
- What are the potential remedies if a violation is found by the European Commission
or the U.K. Competition and Markets Authority in their investigation of automotive
companies?
- By which auditing standards were the consolidated financial statements of Salesforce,
Inc. audited?
- What is the main role of Kroger's Chief Executive Officer in the company?
- source_sentence: The discussion in Hewlett Packard Enterprise's Form 10-K highlights
factors impacting costs and revenues, including easing supply chain constraints,
foreign exchange pressures, inflationary trends, and recent tax developments potentially
affecting their financial outcomes.
sentences:
- Is the outcome of the investigation into Tesla's waste segregation practices currently
determinable?
- How does Hewlett Packard Enterprise justify the exclusion of transformation costs
from its non-GAAP financial measures?
- In the context of Hewlett Packard Enterprise's recent financial discussions, what
factors are expected to impact their operational costs and revenue growth moving
forward?
- source_sentence: Our Records Management and Data Management service revenue growth
is being negatively impacted by declining activity rates as stored records and
tapes are becoming less active and more archival.
sentences:
- How is Iron Mountain addressing the decline in activity rates in their Records
and Data Management services?
- What services do companies that build fiber-based networks provide in the Connectivity
& Platforms markets?
- What business outcomes is HPE focused on accelerating with its technological solutions?
model-index:
- name: BGE base Financial Matryoshka
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.7057142857142857
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8457142857142858
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8785714285714286
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9114285714285715
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7057142857142857
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2819047619047619
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17571428571428568
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09114285714285714
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7057142857142857
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8457142857142858
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8785714285714286
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9114285714285715
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8125296344519609
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7804263038548749
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7839408125709297
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.7071428571428572
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8428571428571429
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8742857142857143
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9114285714285715
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7071428571428572
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.28095238095238095
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17485714285714282
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09114285714285714
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7071428571428572
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8428571428571429
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8742857142857143
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9114285714285715
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8126517351231356
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7807267573696143
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7841188299664252
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.7028571428571428
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8357142857142857
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8685714285714285
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9071428571428571
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7028571428571428
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2785714285714286
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1737142857142857
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09071428571428572
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7028571428571428
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8357142857142857
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8685714285714285
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9071428571428571
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8086618947757659
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7768820861678005
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7806177775944575
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.6914285714285714
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.82
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8557142857142858
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9014285714285715
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6914285714285714
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2733333333333334
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17114285714285712
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09014285714285714
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6914285714285714
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.82
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8557142857142858
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9014285714285715
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7980982703041672
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7650045351473919
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7688564414027702
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.6542857142857142
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7885714285714286
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8328571428571429
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8828571428571429
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6542857142857142
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.26285714285714284
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16657142857142856
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08828571428571427
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6542857142857142
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7885714285714286
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8328571428571429
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8828571428571429
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7689665884678363
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7325351473922898
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7369423610264151
name: Cosine Map@100
---
# BGE base Financial Matryoshka
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("NickyNicky/bge-base-financial-matryoshka")
# Run inference
sentences = [
'Our Records Management and Data Management service revenue growth is being negatively impacted by declining activity rates as stored records and tapes are becoming less active and more archival.',
'How is Iron Mountain addressing the decline in activity rates in their Records and Data Management services?',
'What services do companies that build fiber-based networks provide in the Connectivity & Platforms markets?',
]
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 | 0.7057 |
| cosine_accuracy@3 | 0.8457 |
| cosine_accuracy@5 | 0.8786 |
| cosine_accuracy@10 | 0.9114 |
| cosine_precision@1 | 0.7057 |
| cosine_precision@3 | 0.2819 |
| cosine_precision@5 | 0.1757 |
| cosine_precision@10 | 0.0911 |
| cosine_recall@1 | 0.7057 |
| cosine_recall@3 | 0.8457 |
| cosine_recall@5 | 0.8786 |
| cosine_recall@10 | 0.9114 |
| cosine_ndcg@10 | 0.8125 |
| cosine_mrr@10 | 0.7804 |
| **cosine_map@100** | **0.7839** |
#### 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 | 0.7071 |
| cosine_accuracy@3 | 0.8429 |
| cosine_accuracy@5 | 0.8743 |
| cosine_accuracy@10 | 0.9114 |
| cosine_precision@1 | 0.7071 |
| cosine_precision@3 | 0.281 |
| cosine_precision@5 | 0.1749 |
| cosine_precision@10 | 0.0911 |
| cosine_recall@1 | 0.7071 |
| cosine_recall@3 | 0.8429 |
| cosine_recall@5 | 0.8743 |
| cosine_recall@10 | 0.9114 |
| cosine_ndcg@10 | 0.8127 |
| cosine_mrr@10 | 0.7807 |
| **cosine_map@100** | **0.7841** |
#### 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 | 0.7029 |
| cosine_accuracy@3 | 0.8357 |
| cosine_accuracy@5 | 0.8686 |
| cosine_accuracy@10 | 0.9071 |
| cosine_precision@1 | 0.7029 |
| cosine_precision@3 | 0.2786 |
| cosine_precision@5 | 0.1737 |
| cosine_precision@10 | 0.0907 |
| cosine_recall@1 | 0.7029 |
| cosine_recall@3 | 0.8357 |
| cosine_recall@5 | 0.8686 |
| cosine_recall@10 | 0.9071 |
| cosine_ndcg@10 | 0.8087 |
| cosine_mrr@10 | 0.7769 |
| **cosine_map@100** | **0.7806** |
#### 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 | 0.6914 |
| cosine_accuracy@3 | 0.82 |
| cosine_accuracy@5 | 0.8557 |
| cosine_accuracy@10 | 0.9014 |
| cosine_precision@1 | 0.6914 |
| cosine_precision@3 | 0.2733 |
| cosine_precision@5 | 0.1711 |
| cosine_precision@10 | 0.0901 |
| cosine_recall@1 | 0.6914 |
| cosine_recall@3 | 0.82 |
| cosine_recall@5 | 0.8557 |
| cosine_recall@10 | 0.9014 |
| cosine_ndcg@10 | 0.7981 |
| cosine_mrr@10 | 0.765 |
| **cosine_map@100** | **0.7689** |
#### 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 | 0.6543 |
| cosine_accuracy@3 | 0.7886 |
| cosine_accuracy@5 | 0.8329 |
| cosine_accuracy@10 | 0.8829 |
| cosine_precision@1 | 0.6543 |
| cosine_precision@3 | 0.2629 |
| cosine_precision@5 | 0.1666 |
| cosine_precision@10 | 0.0883 |
| cosine_recall@1 | 0.6543 |
| cosine_recall@3 | 0.7886 |
| cosine_recall@5 | 0.8329 |
| cosine_recall@10 | 0.8829 |
| cosine_ndcg@10 | 0.769 |
| cosine_mrr@10 | 0.7325 |
| **cosine_map@100** | **0.7369** |
<!--
## 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: 6,300 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: 10 tokens</li><li>mean: 46.55 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 20.56 tokens</li><li>max: 42 tokens</li></ul> |
* Samples:
| positive | anchor |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------|
| <code>Internationally, Visa Inc.'s commercial payments volume grew by 23% from $407 billion in 2021 to $500 billion in 2022.</code> | <code>What was the growth rate of Visa Inc.'s commercial payments volume internationally between 2021 and 2022?</code> |
| <code>The consolidated financial statements and accompanying notes listed in Part IV, Item 15(a)(1) of this Annual Report on Form 10-K are included immediately following Part IV hereof.</code> | <code>Where can one find the consolidated financial statements and accompanying notes in the Annual Report on Form 10-K?</code> |
| <code>The additional paid-in capital at the end of 2023 was recorded as $114,519 million.</code> | <code>What was the amount recorded for additional paid-in capital at the end of 2023?</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`: 80
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 15
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: 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`: 80
- `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`: 15
- `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`: False
- `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 | Training Loss | 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 |
|:-------:|:----:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.8101 | 4 | - | 0.7066 | 0.7309 | 0.7390 | 0.6462 | 0.7441 |
| 1.8228 | 9 | - | 0.7394 | 0.7497 | 0.7630 | 0.6922 | 0.7650 |
| 2.0253 | 10 | 2.768 | - | - | - | - | - |
| 2.8354 | 14 | - | 0.7502 | 0.7625 | 0.7767 | 0.7208 | 0.7787 |
| 3.8481 | 19 | - | 0.7553 | 0.7714 | 0.7804 | 0.7234 | 0.7802 |
| 4.0506 | 20 | 1.1294 | - | - | - | - | - |
| 4.8608 | 24 | - | 0.7577 | 0.7769 | 0.7831 | 0.7327 | 0.7858 |
| 5.8734 | 29 | - | 0.7616 | 0.7775 | 0.7832 | 0.7335 | 0.7876 |
| 6.0759 | 30 | 0.7536 | - | - | - | - | - |
| 6.8861 | 34 | - | 0.7624 | 0.7788 | 0.7832 | 0.7352 | 0.7882 |
| 7.8987 | 39 | - | 0.7665 | 0.7795 | 0.7814 | 0.7359 | 0.7861 |
| 8.1013 | 40 | 0.5846 | - | - | - | - | - |
| 8.9114 | 44 | - | 0.7688 | 0.7801 | 0.7828 | 0.7360 | 0.7857 |
| 9.9241 | 49 | - | 0.7698 | 0.7804 | 0.7836 | 0.7367 | 0.7840 |
| 10.1266 | 50 | 0.5187 | - | - | - | - | - |
| 10.9367 | 54 | - | 0.7692 | 0.7801 | 0.7827 | 0.7383 | 0.7837 |
| 11.9494 | 59 | - | 0.7698 | 0.7801 | 0.7834 | 0.7377 | 0.7849 |
| 12.1519 | 60 | 0.4949 | 0.7689 | 0.7806 | 0.7841 | 0.7369 | 0.7839 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.2.0+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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