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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 table indicates that 18,000 deferred shares were granted to
non-employee directors in fiscal 2020, 15,000 in fiscal 2021, and 19,000 in fiscal
2022.
sentences:
- What was the primary reason for the increased audit effort for PCC goodwill and
indefinite-lived intangible assets?
- How many deferred shares were granted to non-employee directors in fiscal 2020,
2021, and 2022?
- What was the total intrinsic value of options exercised in fiscal year 2023?
- source_sentence: In Resource Masking Industries, we expect the profit impact from
lower sales volume to be partially offset by favorable price realization.
sentences:
- By what percentage did Electronic Arts' operating income grow in the fiscal year
ended March 31, 2023?
- What impact is expected on Resource Industries' profit due to lower sales volume?
- How are IBM’s 2023 Annual Report to Stockholders' financial statements made a
part of Form 10-K?
- source_sentence: The actuarial gain during the year ended December 31, 2022 was
primarily related to increases in the discount rate assumptions.
sentences:
- What was the primary reason for the actuarial gain during the year ended December
31, 2022?
- How much did Ford's total assets amount to by December 31, 2023?
- What was the remaining available amount of the share repurchase authorization
as of January 29, 2023?
- source_sentence: Returned $1.7 billion to shareholders through share repurchases
and dividend payments.
sentences:
- What was the carrying amount of investments without readily determinable fair
values as of December 31, 2023?
- What are the significant inputs to the valuation of Goldman Sachs' unsecured short-
and long-term borrowings?
- How much did the company return to shareholders through share repurchases and
dividend payments in 2022?
- source_sentence: The remaining amount available for borrowing under the Revolving
Credit Facility as of December 31, 2023, was $2,245.2 million.
sentences:
- What was the total amount available for borrowing under the Revolving Credit Facility
at Iron Mountain as of December 31, 2023?
- What type of information is included in Note 13 of the Annual Report on Form 10-K?
- How much did local currency revenue increase in Latin America in 2023 compared
to 2022?
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: 0.6828571428571428
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8242857142857143
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8557142857142858
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9057142857142857
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6828571428571428
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2747619047619047
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17114285714285712
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09057142857142855
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6828571428571428
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8242857142857143
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8557142857142858
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9057142857142857
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7963610970343802
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7612930839002267
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7648513048205645
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.68
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8157142857142857
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8542857142857143
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.68
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.27190476190476187
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17085714285714285
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.68
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8157142857142857
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8542857142857143
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7911616934987842
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7562284580498863
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.760087172570928
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.68
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8114285714285714
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8485714285714285
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8971428571428571
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.68
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2704761904761905
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16971428571428568
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0897142857142857
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.68
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8114285714285714
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8485714285714285
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8971428571428571
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7888581850866868
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7542278911564625
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7579536807505182
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.6571428571428571
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.79
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8285714285714286
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8857142857142857
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6571428571428571
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2633333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1657142857142857
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08857142857142856
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6571428571428571
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.79
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8285714285714286
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8857142857142857
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7703812626851927
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.733632653061224
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7378840513025602
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.62
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.77
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8028571428571428
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.85
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.62
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.25666666666666665
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16057142857142856
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.085
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.62
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.77
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8028571428571428
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.85
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.73777886683529
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7016190476190474
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7073607864232172
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("moritzglnr/bge-base-financial-matryoshka")
# Run inference
sentences = [
'The remaining amount available for borrowing under the Revolving Credit Facility as of December 31, 2023, was $2,245.2 million.',
'What was the total amount available for borrowing under the Revolving Credit Facility at Iron Mountain as of December 31, 2023?',
'What type of information is included in Note 13 of the 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 | 0.6829 |
| cosine_accuracy@3 | 0.8243 |
| cosine_accuracy@5 | 0.8557 |
| cosine_accuracy@10 | 0.9057 |
| cosine_precision@1 | 0.6829 |
| cosine_precision@3 | 0.2748 |
| cosine_precision@5 | 0.1711 |
| cosine_precision@10 | 0.0906 |
| cosine_recall@1 | 0.6829 |
| cosine_recall@3 | 0.8243 |
| cosine_recall@5 | 0.8557 |
| cosine_recall@10 | 0.9057 |
| cosine_ndcg@10 | 0.7964 |
| cosine_mrr@10 | 0.7613 |
| **cosine_map@100** | **0.7649** |
#### 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 | 0.68 |
| cosine_accuracy@3 | 0.8157 |
| cosine_accuracy@5 | 0.8543 |
| cosine_accuracy@10 | 0.9 |
| cosine_precision@1 | 0.68 |
| cosine_precision@3 | 0.2719 |
| cosine_precision@5 | 0.1709 |
| cosine_precision@10 | 0.09 |
| cosine_recall@1 | 0.68 |
| cosine_recall@3 | 0.8157 |
| cosine_recall@5 | 0.8543 |
| cosine_recall@10 | 0.9 |
| cosine_ndcg@10 | 0.7912 |
| cosine_mrr@10 | 0.7562 |
| **cosine_map@100** | **0.7601** |
#### 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 | 0.68 |
| cosine_accuracy@3 | 0.8114 |
| cosine_accuracy@5 | 0.8486 |
| cosine_accuracy@10 | 0.8971 |
| cosine_precision@1 | 0.68 |
| cosine_precision@3 | 0.2705 |
| cosine_precision@5 | 0.1697 |
| cosine_precision@10 | 0.0897 |
| cosine_recall@1 | 0.68 |
| cosine_recall@3 | 0.8114 |
| cosine_recall@5 | 0.8486 |
| cosine_recall@10 | 0.8971 |
| cosine_ndcg@10 | 0.7889 |
| cosine_mrr@10 | 0.7542 |
| **cosine_map@100** | **0.758** |
#### 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 | 0.6571 |
| cosine_accuracy@3 | 0.79 |
| cosine_accuracy@5 | 0.8286 |
| cosine_accuracy@10 | 0.8857 |
| cosine_precision@1 | 0.6571 |
| cosine_precision@3 | 0.2633 |
| cosine_precision@5 | 0.1657 |
| cosine_precision@10 | 0.0886 |
| cosine_recall@1 | 0.6571 |
| cosine_recall@3 | 0.79 |
| cosine_recall@5 | 0.8286 |
| cosine_recall@10 | 0.8857 |
| cosine_ndcg@10 | 0.7704 |
| cosine_mrr@10 | 0.7336 |
| **cosine_map@100** | **0.7379** |
#### 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 | 0.62 |
| cosine_accuracy@3 | 0.77 |
| cosine_accuracy@5 | 0.8029 |
| cosine_accuracy@10 | 0.85 |
| cosine_precision@1 | 0.62 |
| cosine_precision@3 | 0.2567 |
| cosine_precision@5 | 0.1606 |
| cosine_precision@10 | 0.085 |
| cosine_recall@1 | 0.62 |
| cosine_recall@3 | 0.77 |
| cosine_recall@5 | 0.8029 |
| cosine_recall@10 | 0.85 |
| cosine_ndcg@10 | 0.7378 |
| cosine_mrr@10 | 0.7016 |
| **cosine_map@100** | **0.7074** |
<!--
## 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: 46.27 tokens</li><li>max: 326 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 20.87 tokens</li><li>max: 51 tokens</li></ul> |
* Samples:
| positive | anchor |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------|
| <code>We utilize a full yield curve approach in the estimation of service and interest costs by applying the specific spot rates along the yield curve used in the determination of the benefit obligation to the relevant projected cash flows. This approach provides a more precise measurement of service and interest costs by improving the correlation between the projected cash flows to the corresponding spot rates along the yield curve. This approach does not affect the measurement of our pension and other post-retirement benefit liabilities but generally results in lower benefit expense in periods when the yield curve is upward sloping.</code> | <code>How does the use of a full yield curve approach in estimating pension costs affect the measurement of liabilities and expenses?</code> |
| <code>Ending | 8,134 | | 8,206 | | 16,340 | | 8,061 | | 8,016 | 16,077</code> | <code>What was the ending store count for the Family Dollar segment after the fiscal year ended January 28, 2023?</code> |
| <code>The company's capital expenditures for 2024 are expected to be approximately $5.7 billion.</code> | <code>How much does the company expect to spend on capital expenditures in 2024?</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 | 1.5661 | - | - | - | - | - |
| 0.9746 | 12 | - | 0.7151 | 0.7378 | 0.7443 | 0.6680 | 0.7546 |
| 1.6244 | 20 | 0.6602 | - | - | - | - | - |
| 1.9492 | 24 | - | 0.7326 | 0.7533 | 0.7564 | 0.7037 | 0.7640 |
| 2.4365 | 30 | 0.4675 | - | - | - | - | - |
| 2.9239 | 36 | - | 0.7384 | 0.7575 | 0.7601 | 0.7086 | 0.7643 |
| 3.2487 | 40 | 0.3891 | - | - | - | - | - |
| **3.8985** | **48** | **-** | **0.7379** | **0.758** | **0.7601** | **0.7074** | **0.7649** |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- 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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