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---
base_model: BAAI/bge-base-en-v1.5
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: There are no relevant matters to disclose under this Item for this
period.
sentences:
- How much did non-cash items contribute to the cash provided by operating activities
in fiscal 2023?
- Are there any legal matters under Item 3 that need to be disclosed for this period?
- What is the primary therapeutic use of Linzess (linaclotide)?
- source_sentence: As of December 31, 2023, we had a $500,000 revolving credit facility
with JPMorgan Chase Bank as administrative agent, with an interest rate based
on the SOFR plus 1.475%, a commitment fee of 0.175% for unused amounts, and conditions
such as maintaining a total leverage ratio of less than 3.0x and a consolidated
fixed charge coverage ratio of greater than 1.5x.
sentences:
- What percentage of U.S. admissions revenues in 2023 was attributed to films from
the company's seven largest movie studio distributors?
- What are the terms of the revolving credit facility agreement with JPMorgan as
of December 31, 2023?
- What was the postpaid churn rate for AT&T Inc. in 2023?
- source_sentence: Gross margin increased from $22,095 million in 2022 to $24,690
million in 2023, amounting to a $2,595 million increase.
sentences:
- How much did the gross margin increase in fiscal year 2023 compared to 2022?
- What percentage of Meta's U.S. workforce in 2023 were represented by people with
disabilities, veterans, and members of the LGBTQ+ community?
- How many FedEx-branded packaging produced in 2022 was third-party certified?
- source_sentence: NHTSA has proposed CAFE standards for model years 2027–2031, and
the EPA has drafted GHG emission standards for 2027–2032. Both sets of standards
are awaiting finalization.
sentences:
- What methods does the company use to advertise its products?
- What types of products does Garmin design, develop, and distribute?
- What are the projected years covered by the new CAFE and GHG emission standards
proposed by NHTSA and the EPA?
- source_sentence: As of December 31, 2023, the fair value and amortized cost, net
of valuation allowance, for the Republic of Korea's government securities were
$1,784 million and $1,723 million respectively.
sentences:
- What was the fair value and amortized cost, net of valuation allowance, for the
Republic of Korea's government securities as of December 31, 2023?
- How does the company advance autonomous vehicle technology?
- What were the key factors affecting the company's cash flow from operations in
fiscal 2023?
model-index:
- name: BGE base Financial Matryoshka
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.6871428571428572
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8285714285714286
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8571428571428571
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9071428571428571
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6871428571428572
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.27619047619047615
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1714285714285714
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0907142857142857
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6871428571428572
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8285714285714286
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8571428571428571
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9071428571428571
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7981646895635455
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7633208616780044
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7670469746658456
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.69
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8171428571428572
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8542857142857143
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9042857142857142
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.69
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2723809523809524
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17085714285714282
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09042857142857141
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.69
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8171428571428572
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8542857142857143
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9042857142857142
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7976622307973412
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7636388888888889
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7675482221709721
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.6857142857142857
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8142857142857143
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8514285714285714
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8957142857142857
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6857142857142857
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2714285714285714
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17028571428571426
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08957142857142855
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6857142857142857
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8142857142857143
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8514285714285714
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8957142857142857
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7916274982255576
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7582437641723355
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7624248845655235
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.6757142857142857
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8414285714285714
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8885714285714286
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6757142857142857
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.26666666666666666
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16828571428571426
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08885714285714286
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6757142857142857
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8414285714285714
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8885714285714286
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.781962439522339
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7478424036281178
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7523517680786094
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.6414285714285715
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7657142857142857
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.7957142857142857
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8585714285714285
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6414285714285715
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2552380952380952
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.15914285714285714
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08585714285714285
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6414285714285715
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7657142857142857
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.7957142857142857
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8585714285714285
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7479917583081255
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7129206349206347
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7185335911194088
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) on the json dataset. 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:**
- json
- **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("Yuki20/bge-base-financial-matryoshka")
# Run inference
sentences = [
"As of December 31, 2023, the fair value and amortized cost, net of valuation allowance, for the Republic of Korea's government securities were $1,784 million and $1,723 million respectively.",
"What was the fair value and amortized cost, net of valuation allowance, for the Republic of Korea's government securities as of December 31, 2023?",
'How does the company advance autonomous vehicle technology?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Information Retrieval
* Dataset: `dim_768`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:----------|
| cosine_accuracy@1 | 0.6871 |
| cosine_accuracy@3 | 0.8286 |
| cosine_accuracy@5 | 0.8571 |
| cosine_accuracy@10 | 0.9071 |
| cosine_precision@1 | 0.6871 |
| cosine_precision@3 | 0.2762 |
| cosine_precision@5 | 0.1714 |
| cosine_precision@10 | 0.0907 |
| cosine_recall@1 | 0.6871 |
| cosine_recall@3 | 0.8286 |
| cosine_recall@5 | 0.8571 |
| cosine_recall@10 | 0.9071 |
| cosine_ndcg@10 | 0.7982 |
| cosine_mrr@10 | 0.7633 |
| **cosine_map@100** | **0.767** |
#### 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 | 0.69 |
| cosine_accuracy@3 | 0.8171 |
| cosine_accuracy@5 | 0.8543 |
| cosine_accuracy@10 | 0.9043 |
| cosine_precision@1 | 0.69 |
| cosine_precision@3 | 0.2724 |
| cosine_precision@5 | 0.1709 |
| cosine_precision@10 | 0.0904 |
| cosine_recall@1 | 0.69 |
| cosine_recall@3 | 0.8171 |
| cosine_recall@5 | 0.8543 |
| cosine_recall@10 | 0.9043 |
| cosine_ndcg@10 | 0.7977 |
| cosine_mrr@10 | 0.7636 |
| **cosine_map@100** | **0.7675** |
#### 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 | 0.6857 |
| cosine_accuracy@3 | 0.8143 |
| cosine_accuracy@5 | 0.8514 |
| cosine_accuracy@10 | 0.8957 |
| cosine_precision@1 | 0.6857 |
| cosine_precision@3 | 0.2714 |
| cosine_precision@5 | 0.1703 |
| cosine_precision@10 | 0.0896 |
| cosine_recall@1 | 0.6857 |
| cosine_recall@3 | 0.8143 |
| cosine_recall@5 | 0.8514 |
| cosine_recall@10 | 0.8957 |
| cosine_ndcg@10 | 0.7916 |
| cosine_mrr@10 | 0.7582 |
| **cosine_map@100** | **0.7624** |
#### 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 | 0.6757 |
| cosine_accuracy@3 | 0.8 |
| cosine_accuracy@5 | 0.8414 |
| cosine_accuracy@10 | 0.8886 |
| cosine_precision@1 | 0.6757 |
| cosine_precision@3 | 0.2667 |
| cosine_precision@5 | 0.1683 |
| cosine_precision@10 | 0.0889 |
| cosine_recall@1 | 0.6757 |
| cosine_recall@3 | 0.8 |
| cosine_recall@5 | 0.8414 |
| cosine_recall@10 | 0.8886 |
| cosine_ndcg@10 | 0.782 |
| cosine_mrr@10 | 0.7478 |
| **cosine_map@100** | **0.7524** |
#### 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 | 0.6414 |
| cosine_accuracy@3 | 0.7657 |
| cosine_accuracy@5 | 0.7957 |
| cosine_accuracy@10 | 0.8586 |
| cosine_precision@1 | 0.6414 |
| cosine_precision@3 | 0.2552 |
| cosine_precision@5 | 0.1591 |
| cosine_precision@10 | 0.0859 |
| cosine_recall@1 | 0.6414 |
| cosine_recall@3 | 0.7657 |
| cosine_recall@5 | 0.7957 |
| cosine_recall@10 | 0.8586 |
| cosine_ndcg@10 | 0.748 |
| cosine_mrr@10 | 0.7129 |
| **cosine_map@100** | **0.7185** |
<!--
## 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.*
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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details
### Training Dataset
#### json
* Dataset: json
* 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: 6 tokens</li><li>mean: 45.58 tokens</li><li>max: 289 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 20.34 tokens</li><li>max: 41 tokens</li></ul> |
* Samples:
| positive | anchor |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|
| <code>Billed business grew significantly over the past two years, increasing from $228.2 billion in 2021 to $281.6 billion in 2022, and reaching $329.5 billion in 2023.</code> | <code>How did billed business figures change from 2021 to 2023 as stated in the text?</code> |
| <code>The Federal Reserve may limit an FHC’s ability to conduct permissible activities if it or any of its depository institution subsidiaries fails to maintain a well-capitalized and well-managed status. If non-compliant after 180 days, the Federal Reserve may require the FHC to divest its depository institution subsidiaries or cease all FHC Activities.</code> | <code>What happens if an FHC does not meet the Federal Reserve's eligibility requirements?</code> |
| <code>For the fiscal year ending January 28, 2023, the basic net income per share was calculated to be $7.24, based on the net income and weighted average number of shares outstanding.</code> | <code>What was the basic net income per share in the fiscal year ending January 28, 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`: 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
- `fp16`: True
- `tf32`: False
- `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`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: False
- `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_768_cosine_map@100 | dim_512_cosine_map@100 | dim_256_cosine_map@100 | dim_128_cosine_map@100 | dim_64_cosine_map@100 |
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
| 0.8122 | 10 | 1.588 | - | - | - | - | - |
| 0.9746 | 12 | - | 0.7593 | 0.7550 | 0.7472 | 0.7347 | 0.6970 |
| 1.6244 | 20 | 0.7059 | - | - | - | - | - |
| 1.9492 | 24 | - | 0.7623 | 0.7652 | 0.7559 | 0.7517 | 0.7127 |
| 2.4365 | 30 | 0.4826 | - | - | - | - | - |
| 2.9239 | 36 | - | 0.7675 | 0.7683 | 0.7603 | 0.7512 | 0.7166 |
| 3.2487 | 40 | 0.3992 | - | - | - | - | - |
| **3.8985** | **48** | **-** | **0.767** | **0.7675** | **0.7624** | **0.7524** | **0.7185** |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.2.0
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.34.2
- 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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