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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: ITEM 7. MANAGEMENT’S DISCUSSION AND ANALYSIS OF FINANCIAL CONDITION
AND RESULTS OF OPERATIONS The following discussion and analysis should be read
in conjunction with the consolidated financial statements and the related notes
included elsewhere in this Annual Report on Form 10-K. For further discussion
of our products and services, technology and competitive strengths, refer to Item
1- Business.
sentences:
- What was the total net automotive cash provided by investing activities in 2023?
- What is the purpose of the Management's Discussion and Analysis of Financial Condition
and Results of Operations section in the Annual Report on Form 10-K?
- What are the components included in the management discussion and analysis of
financial condition and results of operations?
- source_sentence: Kroger is committed to maintaining a net total debt to adjusted
EBITDA ratio target range of 2.30 to 2.50.
sentences:
- What was the remaining available amount of the share repurchase authorization
as of January 29, 2023?
- What range does Kroger aim for its net total debt to adjusted EBITDA ratio?
- What was the starting wage for all entry-level positions in the U.S. as of September
2023?
- source_sentence: Google Cloud operating income of $1.7 billion for 2023.
sentences:
- What was the operating income for Google Cloud in 2023?
- What types of products are offered in Garmin's Fitness segment?
- What was the net sales of the company in fiscal 2022?
- source_sentence: The effective income tax rate for Alphabet Inc. at the end of the
year 2023 was 13.9%.
sentences:
- What was the percentage change in Compute & Networking revenue from fiscal year
2022 to 2023?
- What factors primarily contributed to the increase in non-interest revenues across
all revenue categories?
- What was the effective income tax rate for Alphabet Inc. at the end of the year
2023?
- source_sentence: State legislation increasingly requires PBMs to conduct audits
of network pharmacies regarding claims submitted for payment. Non-compliance could
prevent the recoupment of overpaid amounts, potentially causing financial and
legal repercussions.
sentences:
- What are the potential consequences for a company if its PBMs fail to comply with
pharmacy audit regulations?
- What pages do the Consolidated Financial Statements and their accompanying Notes
and reports appear on in the document?
- What are the primary services provided by the company under the Xfinity, Comcast
Business, and Sky brands?
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: 0.6785714285714286
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8342857142857143
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.88
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9085714285714286
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6785714285714286
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2780952380952381
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.176
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09085714285714284
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6785714285714286
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8342857142857143
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.88
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9085714285714286
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7995179593313807
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7638202947845802
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7674168947978975
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.6685714285714286
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8271428571428572
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8685714285714285
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9128571428571428
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6685714285714286
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2757142857142857
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1737142857142857
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09128571428571428
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6685714285714286
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8271428571428572
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8685714285714285
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9128571428571428
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7954721927324272
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7574353741496596
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7606771546726785
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.6728571428571428
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8142857142857143
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8642857142857143
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9042857142857142
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6728571428571428
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2714285714285714
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17285714285714285
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09042857142857141
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6728571428571428
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8142857142857143
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8642857142857143
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9042857142857142
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7916203877025221
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7552613378684805
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7590698804335085
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.6528571428571428
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8114285714285714
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.85
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8885714285714286
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6528571428571428
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2704761904761904
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16999999999999998
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08885714285714286
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6528571428571428
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8114285714285714
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.85
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8885714285714286
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7754227314755763
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.738630385487528
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7431237490151862
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.6157142857142858
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7614285714285715
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.81
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8642857142857143
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6157142857142858
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2538095238095238
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16199999999999998
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08642857142857142
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6157142857142858
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7614285714285715
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.81
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8642857142857143
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7413954849024657
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.701954648526077
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.707051130510896
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("gauravsirola/bge-base-financial-matryoshka-v1")
# Run inference
sentences = [
'State legislation increasingly requires PBMs to conduct audits of network pharmacies regarding claims submitted for payment. Non-compliance could prevent the recoupment of overpaid amounts, potentially causing financial and legal repercussions.',
'What are the potential consequences for a company if its PBMs fail to comply with pharmacy audit regulations?',
'What pages do the Consolidated Financial Statements and their accompanying Notes and reports appear on in the document?',
]
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 | 0.6786 |
| cosine_accuracy@3 | 0.8343 |
| cosine_accuracy@5 | 0.88 |
| cosine_accuracy@10 | 0.9086 |
| cosine_precision@1 | 0.6786 |
| cosine_precision@3 | 0.2781 |
| cosine_precision@5 | 0.176 |
| cosine_precision@10 | 0.0909 |
| cosine_recall@1 | 0.6786 |
| cosine_recall@3 | 0.8343 |
| cosine_recall@5 | 0.88 |
| cosine_recall@10 | 0.9086 |
| cosine_ndcg@10 | 0.7995 |
| cosine_mrr@10 | 0.7638 |
| **cosine_map@100** | **0.7674** |
#### 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 | 0.6686 |
| cosine_accuracy@3 | 0.8271 |
| cosine_accuracy@5 | 0.8686 |
| cosine_accuracy@10 | 0.9129 |
| cosine_precision@1 | 0.6686 |
| cosine_precision@3 | 0.2757 |
| cosine_precision@5 | 0.1737 |
| cosine_precision@10 | 0.0913 |
| cosine_recall@1 | 0.6686 |
| cosine_recall@3 | 0.8271 |
| cosine_recall@5 | 0.8686 |
| cosine_recall@10 | 0.9129 |
| cosine_ndcg@10 | 0.7955 |
| cosine_mrr@10 | 0.7574 |
| **cosine_map@100** | **0.7607** |
#### 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 | 0.6729 |
| cosine_accuracy@3 | 0.8143 |
| cosine_accuracy@5 | 0.8643 |
| cosine_accuracy@10 | 0.9043 |
| cosine_precision@1 | 0.6729 |
| cosine_precision@3 | 0.2714 |
| cosine_precision@5 | 0.1729 |
| cosine_precision@10 | 0.0904 |
| cosine_recall@1 | 0.6729 |
| cosine_recall@3 | 0.8143 |
| cosine_recall@5 | 0.8643 |
| cosine_recall@10 | 0.9043 |
| cosine_ndcg@10 | 0.7916 |
| cosine_mrr@10 | 0.7553 |
| **cosine_map@100** | **0.7591** |
#### 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 | 0.6529 |
| cosine_accuracy@3 | 0.8114 |
| cosine_accuracy@5 | 0.85 |
| cosine_accuracy@10 | 0.8886 |
| cosine_precision@1 | 0.6529 |
| cosine_precision@3 | 0.2705 |
| cosine_precision@5 | 0.17 |
| cosine_precision@10 | 0.0889 |
| cosine_recall@1 | 0.6529 |
| cosine_recall@3 | 0.8114 |
| cosine_recall@5 | 0.85 |
| cosine_recall@10 | 0.8886 |
| cosine_ndcg@10 | 0.7754 |
| cosine_mrr@10 | 0.7386 |
| **cosine_map@100** | **0.7431** |
#### 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 | 0.6157 |
| cosine_accuracy@3 | 0.7614 |
| cosine_accuracy@5 | 0.81 |
| cosine_accuracy@10 | 0.8643 |
| cosine_precision@1 | 0.6157 |
| cosine_precision@3 | 0.2538 |
| cosine_precision@5 | 0.162 |
| cosine_precision@10 | 0.0864 |
| cosine_recall@1 | 0.6157 |
| cosine_recall@3 | 0.7614 |
| cosine_recall@5 | 0.81 |
| cosine_recall@10 | 0.8643 |
| cosine_ndcg@10 | 0.7414 |
| cosine_mrr@10 | 0.702 |
| **cosine_map@100** | **0.7071** |
<!--
## 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: 7 tokens</li><li>mean: 44.73 tokens</li><li>max: 301 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 20.57 tokens</li><li>max: 41 tokens</li></ul> |
* Samples:
| positive | anchor |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------|
| <code>Net loss was $396.6 million and $973.6 million during the years ended December 31, 2023, and December 31, 2022, respectively.</code> | <code>What was the net loss for the year ended December 31, 2022?</code> |
| <code>Under the 2023 IDA agreement, the service fee on client cash deposits held at the TD Depository Institutions remains at 15 basis points, as it was in the 2019 IDA agreement.</code> | <code>How much is the service fee on client cash deposits held at the TD Depository Institutions under the 2023 IDA agreement?</code> |
| <code>The total shareholders’ deficit is listed as $7,994.8 million in the latest financial statement.</code> | <code>What is the total shareholder's deficit according to the latest financial statement?</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 |
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.8122 | 10 | 1.5585 | - | - | - | - | - |
| 0.9746 | 12 | - | 0.7207 | 0.7441 | 0.7510 | 0.6857 | 0.7493 |
| 1.6244 | 20 | 0.6691 | - | - | - | - | - |
| 1.9492 | 24 | - | 0.7392 | 0.7564 | 0.7601 | 0.7006 | 0.7661 |
| 2.4365 | 30 | 0.4702 | - | - | - | - | - |
| 2.9239 | 36 | - | 0.7430 | 0.7600 | 0.7619 | 0.7065 | 0.7685 |
| 3.2487 | 40 | 0.407 | - | - | - | - | - |
| **3.8985** | **48** | **-** | **0.7431** | **0.7591** | **0.7607** | **0.7071** | **0.7674** |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.6
- 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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