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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: Total net additions to property and equipment for AWS in 2023 amounted
to $24,843 million.
sentences:
- What technological feature helps protect digital transactions in the Visa Token
Service?
- What was the total net addition to property and equipment for AWS in the year
2023?
- By what proportion did net cash used in financing activities increase from 2022
to 2023?
- source_sentence: 'Leases generally contain one or more of the following options,
which the Company can exercise at the end of the initial term: (a) renew the lease
for a defined number of years at the then-fair market rental rate or rate stipulated
in the lease agreement; (b) purchase the property at the then-fair market value
or purchase price stated in the agreement; or (c) a right of first refusal in
the event of a third-party offer.'
sentences:
- What are the requirements for health insurers and group health plans in providing
cost estimates to consumers?
- What options does the company have at the end of the lease term for their leased
properties?
- How much did the company incur in intangible amortization costs related to the
eOne acquisition in 2022?
- source_sentence: We recorded an acquisition termination cost of $1.35 billion in
fiscal year 2023 reflecting the write-off of the prepayment provided at signing.
sentences:
- How much did NVIDIA record as an acquisition termination cost in fiscal year 2023
related to the Arm Share Purchase Agreement?
- What is included in the consolidated financial statements and accompanying notes
mentioned in Part IV, Item 15(a)(1) of the Annual Report on Form 10-K?
- What risks are associated with projecting the effectiveness of internal controls
into future periods as mentioned?
- source_sentence: Item 8 is labeled as Financial Statements and Supplementary Data.
sentences:
- What was the percentage of trading days in 2023 where trading-related revenue
was recorded as positive?
- How is the discount rate for the Family Dollar goodwill impairment evaluation
determined?
- What is the title of Item 8 in the financial document?
- source_sentence: Details about legal proceedings are included in Part II, Item 8,
"Financial Statements and Supplementary Data" of the Annual Report on Form 10-K,
under the caption "Legal Proceedings".
sentences:
- Where can details about legal proceedings be located in an Annual Report on Form
10-K?
- How many stores did AutoZone operate in the United States as of August 26, 2023?
- 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?
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: 0.7071428571428572
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8414285714285714
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.88
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9314285714285714
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7071428571428572
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.28047619047619043
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.176
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09314285714285712
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7071428571428572
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8414285714285714
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.88
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9314285714285714
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8207437059171859
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7853486394557823
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7881907906804949
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.6957142857142857
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8385714285714285
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8757142857142857
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.93
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6957142857142857
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2795238095238095
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17514285714285713
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09299999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6957142857142857
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8385714285714285
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8757142857142857
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.93
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8149439460863356
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7780714285714285
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.781021025356189
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.6885714285714286
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.83
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8742857142857143
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9142857142857143
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6885714285714286
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.27666666666666667
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17485714285714282
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09142857142857141
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6885714285714286
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.83
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8742857142857143
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9142857142857143
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8060991379418679
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7710873015873015
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7751792513774886
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.6771428571428572
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8214285714285714
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8614285714285714
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9142857142857143
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6771428571428572
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2738095238095238
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17228571428571426
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09142857142857141
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6771428571428572
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8214285714285714
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8614285714285714
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9142857142857143
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7979494993398927
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7605890022675734
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7639633810343436
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.6557142857142857
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7871428571428571
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8271428571428572
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8714285714285714
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6557142857142857
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2623809523809524
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1654285714285714
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08714285714285713
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6557142857142857
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7871428571428571
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8271428571428572
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8714285714285714
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7664083634078753
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7326604308390022
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7375736792740525
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("dustyatx/bge-base-financial-matryoshka")
# Run inference
sentences = [
'Details about legal proceedings are included in Part II, Item 8, "Financial Statements and Supplementary Data" of the Annual Report on Form 10-K, under the caption "Legal Proceedings".',
'Where can details about legal proceedings be located in an Annual Report on Form 10-K?',
'How many stores did AutoZone operate in the United States as of August 26, 2023?',
]
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 | 0.7071 |
| cosine_accuracy@3 | 0.8414 |
| cosine_accuracy@5 | 0.88 |
| cosine_accuracy@10 | 0.9314 |
| cosine_precision@1 | 0.7071 |
| cosine_precision@3 | 0.2805 |
| cosine_precision@5 | 0.176 |
| cosine_precision@10 | 0.0931 |
| cosine_recall@1 | 0.7071 |
| cosine_recall@3 | 0.8414 |
| cosine_recall@5 | 0.88 |
| cosine_recall@10 | 0.9314 |
| cosine_ndcg@10 | 0.8207 |
| cosine_mrr@10 | 0.7853 |
| **cosine_map@100** | **0.7882** |
#### 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 | 0.6957 |
| cosine_accuracy@3 | 0.8386 |
| cosine_accuracy@5 | 0.8757 |
| cosine_accuracy@10 | 0.93 |
| cosine_precision@1 | 0.6957 |
| cosine_precision@3 | 0.2795 |
| cosine_precision@5 | 0.1751 |
| cosine_precision@10 | 0.093 |
| cosine_recall@1 | 0.6957 |
| cosine_recall@3 | 0.8386 |
| cosine_recall@5 | 0.8757 |
| cosine_recall@10 | 0.93 |
| cosine_ndcg@10 | 0.8149 |
| cosine_mrr@10 | 0.7781 |
| **cosine_map@100** | **0.781** |
#### 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 | 0.6886 |
| cosine_accuracy@3 | 0.83 |
| cosine_accuracy@5 | 0.8743 |
| cosine_accuracy@10 | 0.9143 |
| cosine_precision@1 | 0.6886 |
| cosine_precision@3 | 0.2767 |
| cosine_precision@5 | 0.1749 |
| cosine_precision@10 | 0.0914 |
| cosine_recall@1 | 0.6886 |
| cosine_recall@3 | 0.83 |
| cosine_recall@5 | 0.8743 |
| cosine_recall@10 | 0.9143 |
| cosine_ndcg@10 | 0.8061 |
| cosine_mrr@10 | 0.7711 |
| **cosine_map@100** | **0.7752** |
#### 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 | 0.6771 |
| cosine_accuracy@3 | 0.8214 |
| cosine_accuracy@5 | 0.8614 |
| cosine_accuracy@10 | 0.9143 |
| cosine_precision@1 | 0.6771 |
| cosine_precision@3 | 0.2738 |
| cosine_precision@5 | 0.1723 |
| cosine_precision@10 | 0.0914 |
| cosine_recall@1 | 0.6771 |
| cosine_recall@3 | 0.8214 |
| cosine_recall@5 | 0.8614 |
| cosine_recall@10 | 0.9143 |
| cosine_ndcg@10 | 0.7979 |
| cosine_mrr@10 | 0.7606 |
| **cosine_map@100** | **0.764** |
#### 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 | 0.6557 |
| cosine_accuracy@3 | 0.7871 |
| cosine_accuracy@5 | 0.8271 |
| cosine_accuracy@10 | 0.8714 |
| cosine_precision@1 | 0.6557 |
| cosine_precision@3 | 0.2624 |
| cosine_precision@5 | 0.1654 |
| cosine_precision@10 | 0.0871 |
| cosine_recall@1 | 0.6557 |
| cosine_recall@3 | 0.7871 |
| cosine_recall@5 | 0.8271 |
| cosine_recall@10 | 0.8714 |
| cosine_ndcg@10 | 0.7664 |
| cosine_mrr@10 | 0.7327 |
| **cosine_map@100** | **0.7376** |
<!--
## 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: 45.94 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 20.7 tokens</li><li>max: 42 tokens</li></ul> |
* Samples:
| positive | anchor |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>The company must continuously strengthen its capabilities in marketing and innovation to compete in a digital environment and maintain brand loyalty and marketallability. In addition, it is increasing its investments in e-commerce to support retail and meal delivery services, offering more package sizes that are fit-for-purpose for online sales and shifting more consumer and trade promotions to digital.</code> | <code>What strategies is the company employing to enhance its competitiveness in a digital environment?</code> |
| <code>Fedflowing expanded or relocated its hub and linehaul network, FedEx Ground also introduced new safety technologies, set new driver standards, and made operational enhancements for safer handling of heavy items.</code> | <code>What specific changes has FedEx Ground made for vehicle and driver safety?</code> |
| <code>The debt financing, which is being provided by a syndicate of Chinese financial institutions, contains certain covenants and a maximum borrowing limit of ¥29.7 billion RMB (approximately $4.2 billion).</code> | <code>What is the maximum borrowing limit of the debt financing provided by the syndicate of Chinese financial institutions for Universal Beijing Resort?</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
- `torch_empty_cache_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
- `eval_on_start`: False
- `eval_use_gather_object`: 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 | 1.5212 | - | - | - | - | - |
| 0.9746 | 12 | - | 0.7439 | 0.7556 | 0.7670 | 0.7142 | 0.7717 |
| 1.6244 | 20 | 0.6418 | - | - | - | - | - |
| 1.9492 | 24 | - | 0.7592 | 0.7743 | 0.7787 | 0.7331 | 0.7839 |
| 2.4365 | 30 | 0.4411 | - | - | - | - | - |
| 2.9239 | 36 | - | 0.7623 | 0.7757 | 0.7816 | 0.7365 | 0.7902 |
| 3.2487 | 40 | 0.3917 | - | - | - | - | - |
| **3.8985** | **48** | **-** | **0.764** | **0.7752** | **0.781** | **0.7376** | **0.7882** |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.11.9
- Sentence Transformers: 3.0.1
- Transformers: 4.44.2
- PyTorch: 2.4.0+cu121
- Accelerate: 0.33.0
- Datasets: 2.21.0
- 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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