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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:6300
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-base-en
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 of January 31, 2023, the Company's net operating loss and capital
loss carryforwards totaled approximately $32.3 billion.
sentences:
- What was the percentage change in general and administrative expenses in 2023
compared to 2022?
- What was the amount of the company's net operating loss and capital loss carryforwards
as of January 31, 2023?
- What are common challenges in pharmaceutical research and development?
- source_sentence: A 0.50% increase in completion factors, which consider aspects
like claim levels and processing cycles, raises medical costs payable by $585
million as of December 31, 2023.
sentences:
- What were the total assets of Hasbro, Inc. as of December 31, 2023?
- How does a 0.50% increase in completion factors impact medical costs payable as
of December 31, 2023?
- By what percentage did Gaming revenue change in fiscal year 2023 compared to fiscal
year 2022?
- source_sentence: Alex G. Balazs was appointed as the Executive Vice President and
Chief Technology Officer effective September 5, 2023.
sentences:
- When was Alex G. Balazs appointed as the Executive Vice President and Chief Technology
Officer?
- What was AMC's minimum liquidity requirement under the Credit Agreement?
- What was the nature of the legal action initiated by Aqua-Chem against the company
in Wisconsin on the same day the company filed its lawsuit?
- source_sentence: Item 8. Financial Statements and Supplementary Data
sentences:
- How did the carrying amount of goodwill change from March 31, 2022 to March 31,
2023?
- What types of revenue does the payments company generate from its various products
and services?
- What is the content of Item 8 in a financial document?
- source_sentence: The company offers Medicare eligible persons under HMO, PPO, Private
Fee-For-Service, or PFFS, and Special Needs Plans, including Dual Eligible Special
Needs, or D-SNP, plans in exchange for contractual payments received from CMS.
With each of these products, the beneficiary receives benefits in excess of Medicare
FFS, typically including reduced cost sharing, enhanced prescription drug benefits,
care coordination, data analysis techniques to help identify member needs, complex
case management, tools to guide members in their health care decisions, care management
programs, wellness and prevention programs and, in some instances, a reduced monthly
Part B premium. Most Medicare Advantage plans offer the prescription drug benefit
under Part D as part of the basic plan, subject to cost sharing and other limitations.
sentences:
- What types of Medicare plans does the company offer and what are the key benefits
provided?
- What were the total cash discounts provided by AbbVie in 2023, 2022, and 2021?
- How does a company account for potential liabilities from legal proceedings in
its financial statements?
pipeline_tag: sentence-similarity
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: 0.7028571428571428
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8371428571428572
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.87
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9114285714285715
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7028571428571428
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.27904761904761904
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.174
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09114285714285714
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7028571428571428
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8371428571428572
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.87
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9114285714285715
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8100174465587288
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7773446712018138
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7807079942767247
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.6942857142857143
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.83
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.87
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9128571428571428
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6942857142857143
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.27666666666666667
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.174
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09128571428571428
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6942857142857143
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.83
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.87
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9128571428571428
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8078520466243649
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7740147392290249
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7772770435826438
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.6914285714285714
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: 0.9114285714285715
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6914285714285714
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: 0.09114285714285712
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6914285714285714
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: 0.9114285714285715
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8048419939996826
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7705011337868479
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7738179161222841
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.6814285714285714
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.82
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8628571428571429
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.91
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6814285714285714
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2733333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17257142857142854
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09099999999999998
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6814285714285714
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.82
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8628571428571429
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.91
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7983213130859076
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7624348072562357
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7654098753888775
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.6628571428571428
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7985714285714286
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8414285714285714
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8971428571428571
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6628571428571428
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.26619047619047614
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16828571428571426
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0897142857142857
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6628571428571428
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7985714285714286
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8414285714285714
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8971428571428571
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7801763622372425
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7428265306122449
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7467214067895231
name: Cosine Map@100
---
# BGE base Financial Matryoshka
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en). 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](https://huggingface.co/BAAI/bge-base-en) <!-- at revision b737bf5dcc6ee8bdc530531266b4804a5d77b5d8 -->
- **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("riphunter7001x/bge-base-financial")
# Run inference
sentences = [
'The company offers Medicare eligible persons under HMO, PPO, Private Fee-For-Service, or PFFS, and Special Needs Plans, including Dual Eligible Special Needs, or D-SNP, plans in exchange for contractual payments received from CMS. With each of these products, the beneficiary receives benefits in excess of Medicare FFS, typically including reduced cost sharing, enhanced prescription drug benefits, care coordination, data analysis techniques to help identify member needs, complex case management, tools to guide members in their health care decisions, care management programs, wellness and prevention programs and, in some instances, a reduced monthly Part B premium. Most Medicare Advantage plans offer the prescription drug benefit under Part D as part of the basic plan, subject to cost sharing and other limitations.',
'What types of Medicare plans does the company offer and what are the key benefits provided?',
'What were the total cash discounts provided by AbbVie in 2023, 2022, and 2021?',
]
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 | 0.7029 |
| cosine_accuracy@3 | 0.8371 |
| cosine_accuracy@5 | 0.87 |
| cosine_accuracy@10 | 0.9114 |
| cosine_precision@1 | 0.7029 |
| cosine_precision@3 | 0.279 |
| cosine_precision@5 | 0.174 |
| cosine_precision@10 | 0.0911 |
| cosine_recall@1 | 0.7029 |
| cosine_recall@3 | 0.8371 |
| cosine_recall@5 | 0.87 |
| cosine_recall@10 | 0.9114 |
| cosine_ndcg@10 | 0.81 |
| cosine_mrr@10 | 0.7773 |
| **cosine_map@100** | **0.7807** |
#### 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 | 0.6943 |
| cosine_accuracy@3 | 0.83 |
| cosine_accuracy@5 | 0.87 |
| cosine_accuracy@10 | 0.9129 |
| cosine_precision@1 | 0.6943 |
| cosine_precision@3 | 0.2767 |
| cosine_precision@5 | 0.174 |
| cosine_precision@10 | 0.0913 |
| cosine_recall@1 | 0.6943 |
| cosine_recall@3 | 0.83 |
| cosine_recall@5 | 0.87 |
| cosine_recall@10 | 0.9129 |
| cosine_ndcg@10 | 0.8079 |
| cosine_mrr@10 | 0.774 |
| **cosine_map@100** | **0.7773** |
#### 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 | 0.6914 |
| cosine_accuracy@3 | 0.8271 |
| cosine_accuracy@5 | 0.8686 |
| cosine_accuracy@10 | 0.9114 |
| cosine_precision@1 | 0.6914 |
| cosine_precision@3 | 0.2757 |
| cosine_precision@5 | 0.1737 |
| cosine_precision@10 | 0.0911 |
| cosine_recall@1 | 0.6914 |
| cosine_recall@3 | 0.8271 |
| cosine_recall@5 | 0.8686 |
| cosine_recall@10 | 0.9114 |
| cosine_ndcg@10 | 0.8048 |
| cosine_mrr@10 | 0.7705 |
| **cosine_map@100** | **0.7738** |
#### 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 | 0.6814 |
| cosine_accuracy@3 | 0.82 |
| cosine_accuracy@5 | 0.8629 |
| cosine_accuracy@10 | 0.91 |
| cosine_precision@1 | 0.6814 |
| cosine_precision@3 | 0.2733 |
| cosine_precision@5 | 0.1726 |
| cosine_precision@10 | 0.091 |
| cosine_recall@1 | 0.6814 |
| cosine_recall@3 | 0.82 |
| cosine_recall@5 | 0.8629 |
| cosine_recall@10 | 0.91 |
| cosine_ndcg@10 | 0.7983 |
| cosine_mrr@10 | 0.7624 |
| **cosine_map@100** | **0.7654** |
#### 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 | 0.6629 |
| cosine_accuracy@3 | 0.7986 |
| cosine_accuracy@5 | 0.8414 |
| cosine_accuracy@10 | 0.8971 |
| cosine_precision@1 | 0.6629 |
| cosine_precision@3 | 0.2662 |
| cosine_precision@5 | 0.1683 |
| cosine_precision@10 | 0.0897 |
| cosine_recall@1 | 0.6629 |
| cosine_recall@3 | 0.7986 |
| cosine_recall@5 | 0.8414 |
| cosine_recall@10 | 0.8971 |
| cosine_ndcg@10 | 0.7802 |
| cosine_mrr@10 | 0.7428 |
| **cosine_map@100** | **0.7467** |
<!--
## 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: 2 tokens</li><li>mean: 45.98 tokens</li><li>max: 208 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 20.76 tokens</li><li>max: 43 tokens</li></ul> |
* Samples:
| positive | anchor |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Adjusted EBITDA does not reflect costs associated with product recall related matters including adjustments to the return reserves, inventory write-downs, logistics costs associated with Member requests, the cost to move the recalled product for those that elect the option, subscription waiver costs of service, and recall-related hardware development and repair costs.</code> | <code>What specific costs associated with product recalls are excluded from Adjusted EBITDA?</code> |
| <code>The Company sold $17,704 million and $10,709 million of trade accounts receivables under this program during the years ended December 31, 2023 and 2022, respectively.</code> | <code>How much did the Company sell in trade accounts receivables in the year ended December 31, 2023?</code> |
| <code>Free cash flow less equipment finance leases and principal repayments of all other finance leases and financing obligations was -$12,786 million in 2022 and improved to $35,549 million in 2023.</code> | <code>How did the free cash flow less equipment finance leases and principal repayments of all other finance leases and financing obligations change from 2022 to 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`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 10
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 5e-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`: 10
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `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`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `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
- `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 |
|:------:|:----:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.2538 | 100 | 2.4219 | 0.7320 | 0.7542 | 0.7582 | 0.6929 | 0.7561 |
| 0.5076 | 200 | 0.468 | 0.7343 | 0.7543 | 0.7574 | 0.7044 | 0.7569 |
| 0.7614 | 300 | 0.3159 | 0.7569 | 0.7691 | 0.7749 | 0.7288 | 0.7713 |
| 1.0152 | 400 | 0.317 | 0.7455 | 0.7607 | 0.7646 | 0.7124 | 0.7643 |
| 1.2690 | 500 | 0.2062 | 0.7465 | 0.7691 | 0.7741 | 0.7211 | 0.7748 |
| 1.5228 | 600 | 0.1075 | 0.7495 | 0.7599 | 0.7696 | 0.7214 | 0.7697 |
| 1.7766 | 700 | 0.1079 | 0.7572 | 0.7660 | 0.7752 | 0.7287 | 0.7764 |
| 2.0305 | 800 | 0.0477 | 0.7447 | 0.7696 | 0.7760 | 0.7211 | 0.7786 |
| 2.2843 | 900 | 0.0547 | 0.7569 | 0.7728 | 0.7757 | 0.7406 | 0.7746 |
| 2.5381 | 1000 | 0.0283 | 0.7668 | 0.7756 | 0.7823 | 0.7414 | 0.7841 |
| 2.7919 | 1100 | 0.0268 | 0.7540 | 0.7673 | 0.7766 | 0.7432 | 0.7748 |
| 3.0457 | 1200 | 0.0201 | 0.7633 | 0.7739 | 0.7799 | 0.7411 | 0.7775 |
| 3.2995 | 1300 | 0.0174 | 0.7635 | 0.7745 | 0.7856 | 0.7469 | 0.7851 |
| 3.5533 | 1400 | 0.0161 | 0.7595 | 0.7765 | 0.7825 | 0.7412 | 0.7782 |
| 3.8071 | 1500 | 0.0071 | 0.7552 | 0.7680 | 0.7754 | 0.7395 | 0.7739 |
| 4.0609 | 1600 | 0.009 | 0.7633 | 0.7767 | 0.7834 | 0.7423 | 0.7843 |
| 4.3147 | 1700 | 0.0079 | 0.7639 | 0.7714 | 0.7770 | 0.7414 | 0.7728 |
| 4.5685 | 1800 | 0.0109 | 0.7662 | 0.7775 | 0.7845 | 0.7369 | 0.7843 |
| 4.8223 | 1900 | 0.0024 | 0.7674 | 0.7732 | 0.7776 | 0.7425 | 0.7810 |
| 5.0761 | 2000 | 0.0052 | 0.7729 | 0.7746 | 0.7820 | 0.7455 | 0.7849 |
| 5.3299 | 2100 | 0.0022 | 0.7615 | 0.7754 | 0.7813 | 0.7446 | 0.7862 |
| 5.5838 | 2200 | 0.0065 | 0.7691 | 0.7761 | 0.7809 | 0.7437 | 0.7777 |
| 5.8376 | 2300 | 0.0011 | 0.7672 | 0.7728 | 0.7757 | 0.7446 | 0.7772 |
| 6.0914 | 2400 | 0.0046 | 0.7671 | 0.7778 | 0.7805 | 0.7494 | 0.7838 |
| 6.3452 | 2500 | 0.0013 | 0.7655 | 0.7732 | 0.7780 | 0.7478 | 0.7806 |
| 6.5990 | 2600 | 0.0058 | 0.7673 | 0.7753 | 0.7779 | 0.7542 | 0.7797 |
| 6.8528 | 2700 | 0.001 | 0.7654 | 0.7716 | 0.7738 | 0.7535 | 0.7776 |
| 7.1066 | 2800 | 0.0071 | 0.7684 | 0.7754 | 0.7792 | 0.7518 | 0.7824 |
| 7.3604 | 2900 | 0.001 | 0.7723 | 0.7765 | 0.7814 | 0.7502 | 0.7826 |
| 7.6142 | 3000 | 0.0028 | 0.7720 | 0.7754 | 0.7807 | 0.7498 | 0.7806 |
| 7.8680 | 3100 | 0.0007 | 0.7685 | 0.7728 | 0.7773 | 0.7475 | 0.7816 |
| 8.1218 | 3200 | 0.004 | 0.7690 | 0.7741 | 0.7773 | 0.7496 | 0.7806 |
| 8.3756 | 3300 | 0.0006 | 0.7683 | 0.7723 | 0.7755 | 0.7491 | 0.7791 |
| 8.6294 | 3400 | 0.0011 | 0.7678 | 0.7724 | 0.7756 | 0.7508 | 0.7804 |
| 8.8832 | 3500 | 0.0006 | 0.7655 | 0.7721 | 0.7769 | 0.7467 | 0.7825 |
| 9.1371 | 3600 | 0.0013 | 0.7674 | 0.7751 | 0.7788 | 0.7463 | 0.7802 |
| 9.3909 | 3700 | 0.0006 | 0.7664 | 0.7741 | 0.7793 | 0.7468 | 0.7821 |
| 9.6447 | 3800 | 0.0011 | 0.7662 | 0.7753 | 0.7782 | 0.7481 | 0.7803 |
| 9.8985 | 3900 | 0.0005 | 0.7654 | 0.7738 | 0.7773 | 0.7467 | 0.7807 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.3.0+cu121
- Accelerate: 0.31.0
- Datasets: 2.19.2
- 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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