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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: The lawsuits were filed in the wake of media reports that the U.S.
Department of Justice had served civil investigative demands upon these carriers
seeking documents and information relating to this subject.
sentences:
- What type of details does Note 15 of the Consolidated Financial Statements provide?
- What action did the U.S. Department of Justice take in relation to the antitrust
allegations against Delta, American, United, and Southwest airlines?
- What does the index in a financial report indicate?
- source_sentence: Unearned Revenue comprises mainly unearned revenue related to volume
licensing programs, which may include Software Assurance ("SA") and cloud services.
sentences:
- What was the total number of Starbucks employees worldwide as of October 1, 2023?
- What primarily comprises unearned revenue according to the discussed financial
statements?
- How are impairment charges for the years 2021, 2022, and 2023 recorded for restaurants
and offices, and what is their impact on financial statements?
- source_sentence: Total sales and revenues for 2023 were $67.060 billion, an increase
of $7.633 billion, or 13 percent, compared with $59.427 billion in 2022.
sentences:
- How much did Caterpillar's total sales and revenues increase by in 2023 compared
to 2022?
- What is included in the cost of revenues for Google?
- What entity audited the company's consolidated financial statements?
- source_sentence: 'Weighted average remaining lease term and discount rate at March
31, 2023 and 2022 are as follows: At March 31, 2023 - Lease term: 7.5 years, Discount
rate: 3.3%; At March 31, 2022 - Lease term: 6.8 years, Discount rate: 2.5%.'
sentences:
- What operating system is used for the Company's iPhone line?
- What was the SRO's accrued amount as a receivable for CAT implementation expenses
as of December 31, 2023?
- What were the lease terms and discount rates for operating leases as of March
31, 2023 and 2022?
- source_sentence: During 2023, continuing investing activities generated $240 million,
significantly influenced by $14.5 billion received from the maturities and sales
of investments, with expenditures of $13.9 billion on investments and $456 million
on property and equipment.
sentences:
- What significant financial activity occurred in continuing investing activities
in 2023?
- What indicates where to find information about legal proceedings in the consolidated
financial statements of an Annual Report on Form 10-K?
- How much cash, cash equivalents, and unrestricted marketable securities did the
company have as of September 30, 2023?
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: 0.6871428571428572
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8171428571428572
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8542857142857143
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9042857142857142
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6871428571428572
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.27238095238095233
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17085714285714282
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09042857142857141
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6871428571428572
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8171428571428572
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8542857142857143
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9042857142857142
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7940751364022482
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7589863945578228
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7632147157763912
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.6828571428571428
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8142857142857143
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8542857142857143
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9014285714285715
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6828571428571428
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2714285714285714
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17085714285714285
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09014285714285714
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6828571428571428
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8142857142857143
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8542857142857143
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9014285714285715
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7923306650275913
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7573690476190474
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7616425347398016
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.6642857142857143
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8042857142857143
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8557142857142858
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8971428571428571
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6642857142857143
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2680952380952381
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17114285714285712
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0897142857142857
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6642857142857143
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8042857142857143
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8557142857142858
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8971428571428571
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.781836757101301
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7447794784580494
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7491639960128558
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.6457142857142857
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7828571428571428
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.83
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8857142857142857
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6457142857142857
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.26095238095238094
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16599999999999998
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08857142857142856
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6457142857142857
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7828571428571428
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.83
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8857142857142857
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7638551069830676
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7249971655328794
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7295529486648893
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.6171428571428571
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7385714285714285
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.7928571428571428
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.84
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6171428571428571
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.24619047619047615
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.15857142857142856
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08399999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6171428571428571
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7385714285714285
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.7928571428571428
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.84
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7256498773041486
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6893407029478454
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6948404384614005
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("Liu-Xiang/bge-base-financial-matryoshka")
# Run inference
sentences = [
'During 2023, continuing investing activities generated $240 million, significantly influenced by $14.5 billion received from the maturities and sales of investments, with expenditures of $13.9 billion on investments and $456 million on property and equipment.',
'What significant financial activity occurred in continuing investing activities in 2023?',
'What indicates where to find information about legal proceedings in the consolidated financial statements of an Annual Report on Form 10-K?',
]
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 | 0.6871 |
| cosine_accuracy@3 | 0.8171 |
| cosine_accuracy@5 | 0.8543 |
| cosine_accuracy@10 | 0.9043 |
| cosine_precision@1 | 0.6871 |
| cosine_precision@3 | 0.2724 |
| cosine_precision@5 | 0.1709 |
| cosine_precision@10 | 0.0904 |
| cosine_recall@1 | 0.6871 |
| cosine_recall@3 | 0.8171 |
| cosine_recall@5 | 0.8543 |
| cosine_recall@10 | 0.9043 |
| cosine_ndcg@10 | 0.7941 |
| cosine_mrr@10 | 0.759 |
| **cosine_map@100** | **0.7632** |
#### 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 | 0.6829 |
| cosine_accuracy@3 | 0.8143 |
| cosine_accuracy@5 | 0.8543 |
| cosine_accuracy@10 | 0.9014 |
| cosine_precision@1 | 0.6829 |
| cosine_precision@3 | 0.2714 |
| cosine_precision@5 | 0.1709 |
| cosine_precision@10 | 0.0901 |
| cosine_recall@1 | 0.6829 |
| cosine_recall@3 | 0.8143 |
| cosine_recall@5 | 0.8543 |
| cosine_recall@10 | 0.9014 |
| cosine_ndcg@10 | 0.7923 |
| cosine_mrr@10 | 0.7574 |
| **cosine_map@100** | **0.7616** |
#### 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 | 0.6643 |
| cosine_accuracy@3 | 0.8043 |
| cosine_accuracy@5 | 0.8557 |
| cosine_accuracy@10 | 0.8971 |
| cosine_precision@1 | 0.6643 |
| cosine_precision@3 | 0.2681 |
| cosine_precision@5 | 0.1711 |
| cosine_precision@10 | 0.0897 |
| cosine_recall@1 | 0.6643 |
| cosine_recall@3 | 0.8043 |
| cosine_recall@5 | 0.8557 |
| cosine_recall@10 | 0.8971 |
| cosine_ndcg@10 | 0.7818 |
| cosine_mrr@10 | 0.7448 |
| **cosine_map@100** | **0.7492** |
#### 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 | 0.6457 |
| cosine_accuracy@3 | 0.7829 |
| cosine_accuracy@5 | 0.83 |
| cosine_accuracy@10 | 0.8857 |
| cosine_precision@1 | 0.6457 |
| cosine_precision@3 | 0.261 |
| cosine_precision@5 | 0.166 |
| cosine_precision@10 | 0.0886 |
| cosine_recall@1 | 0.6457 |
| cosine_recall@3 | 0.7829 |
| cosine_recall@5 | 0.83 |
| cosine_recall@10 | 0.8857 |
| cosine_ndcg@10 | 0.7639 |
| cosine_mrr@10 | 0.725 |
| **cosine_map@100** | **0.7296** |
#### 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 | 0.6171 |
| cosine_accuracy@3 | 0.7386 |
| cosine_accuracy@5 | 0.7929 |
| cosine_accuracy@10 | 0.84 |
| cosine_precision@1 | 0.6171 |
| cosine_precision@3 | 0.2462 |
| cosine_precision@5 | 0.1586 |
| cosine_precision@10 | 0.084 |
| cosine_recall@1 | 0.6171 |
| cosine_recall@3 | 0.7386 |
| cosine_recall@5 | 0.7929 |
| cosine_recall@10 | 0.84 |
| cosine_ndcg@10 | 0.7256 |
| cosine_mrr@10 | 0.6893 |
| **cosine_map@100** | **0.6948** |
<!--
## 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: 8 tokens</li><li>mean: 45.54 tokens</li><li>max: 288 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 20.38 tokens</li><li>max: 46 tokens</li></ul> |
* Samples:
| positive | anchor |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>If the discount rate used to calculate the present value of these reserves changed by 25 basis points, net income would have been affected by approximately $1.1 million for fiscal 2023.</code> | <code>By what amount would net income for fiscal 2023 be affected if the discount rate used for calculating the present value of reserves changed by 25 basis points?</code> |
| <code>Net revenue | $ | 8,110,518 | | | $ | 6,256,617 | | 100.0 | % | 100.0 | % | $ | 1,853,901 | 29.6 | %</code> | <code>What was the percentage increase in net revenue in 2022 compared to 2021?</code> |
| <code>Item 8 covers Financial Statements and Supplementary Data.</code> | <code>What is included in Item 8 of the document?</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`: 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
- `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 |
|:--------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.96 | 3 | - | 0.6943 | 0.7200 | 0.7341 | 0.6337 | 0.7346 |
| 1.92 | 6 | - | 0.7178 | 0.7393 | 0.7525 | 0.6764 | 0.7513 |
| 2.88 | 9 | - | 0.7280 | 0.7468 | 0.7584 | 0.6926 | 0.7611 |
| 3.2 | 10 | 3.3659 | - | - | - | - | - |
| **3.84** | **12** | **-** | **0.7296** | **0.7492** | **0.7616** | **0.6948** | **0.7632** |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.9.18
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.32.1
- 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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