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---
base_model: mixedbread-ai/mxbai-embed-large-v1
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:3550
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: At the end of 2023, Alphabet Inc. reported total debts amounting
to $14.2 billion, compared to $10.9 billion at the end of 2022.
sentences:
- What was the total debt of Alphabet Inc. as of the end of 2023?
- What was ExxonMobil's contribution to the energy production in the Energy sector
during 2020?
- Describe Amazon's revenue growth in 2023?
- source_sentence: In 2022, Pfizer strategically managed cash flow from investments
by utilizing operating cash flow, issuing new debt, and through the monetization
of certain non-core assets. This approach of diversifying the source of funding
for investments was done to minimize risk and uncertainty in economic conditions.
sentences:
- How much capital expenditure did AUX Energy invest in renewable energy projects
in 2022?
- What effect did the 2023 market downturn have on Amazon's retail and cloud segments?
- How did Pfizer manage cash flows from investments in 2022?
- source_sentence: The primary revenue generators for JPMorgan Chase for the fiscal
year 2023 were the Corporate & Investment Bank (CIB) and the Asset & Wealth Management
(AWM) sectors. The CIB sector benefited from a rise in merger and acquisition
activities, while AWM saw large net inflows.
sentences:
- What is General Electric's strategic priority for its Aviation business segment?
- Which sectors contributed the most to the revenue of JPMorgan Chase for FY 2023?
- What is the principal activity of Apple Inc.?
- source_sentence: For the fiscal year 2023, Microsoft's Intelligent Cloud segment
generated revenues of $58 billion, demonstrating solid growth fueled by strong
demand for cloud services and server products.
sentences:
- What is the primary strategy of McDonald’s to drive growth in the future?
- What impact did the increase in gold prices have on Newmont Corporation's revenue
in 2023?
- What was the revenue generated by Microsoft's Intelligent Cloud segment for fiscal
year 2023?
- source_sentence: Microsoft, in their latest press release, revealed that they are
anticipating a revenue growth of approximately 12% for the fiscal year ending
in 2024.
sentences:
- What is Microsoft's projected revenue growth for fiscal year 2024?
- What is the fair value of equity method investments of Microsoft in the fiscal
year 2025?
- What was the impact of COVID-19 on Zoom's profits?
model-index:
- name: mxbai-embed-large-v1-financial-rag-matryoshka
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 1024
type: dim_1024
metrics:
- type: cosine_accuracy@1
value: 0.8455696202531645
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9392405063291139
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9670886075949368
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9898734177215189
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.8455696202531645
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.31308016877637135
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19341772151898737
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0989873417721519
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.8455696202531645
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.9392405063291139
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9670886075949368
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9898734177215189
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9212281141643793
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.898873819570022
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8993853803492357
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.8455696202531645
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9392405063291139
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9670886075949368
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9898734177215189
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.8455696202531645
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.3130801687763713
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1934177215189873
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0989873417721519
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.8455696202531645
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.9392405063291139
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9670886075949368
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9898734177215189
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9217284365901642
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8994826200522402
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8999494134557425
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.8405063291139241
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9367088607594937
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9645569620253165
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9898734177215189
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.8405063291139241
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.31223628691983124
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19291139240506328
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0989873417721519
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.8405063291139241
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.9367088607594937
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9645569620253165
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9898734177215189
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9186273598847787
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8954631303998389
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8958871142668611
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.8455696202531645
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9392405063291139
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9645569620253165
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9898734177215189
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.8455696202531645
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.3130801687763713
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19291139240506328
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0989873417721519
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.8455696202531645
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.9392405063291139
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9645569620253165
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9898734177215189
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9201161947922436
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8975597749648381
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8979721416614026
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.8405063291139241
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9417721518987342
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9645569620253165
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9848101265822785
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.8405063291139241
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.3139240506329114
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19291139240506328
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09848101265822784
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.8405063291139241
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.9417721518987342
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9645569620253165
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9848101265822785
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9170562815583235
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8948693992364878
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8957325656059834
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.8405063291139241
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9316455696202531
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9569620253164557
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9822784810126582
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.8405063291139241
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.3105485232067511
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19139240506329114
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09822784810126582
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.8405063291139241
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.9316455696202531
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9569620253164557
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9822784810126582
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9153318022971121
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8934589109905566
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8943102728098851
name: Cosine Map@100
---
# mxbai-embed-large-v1-financial-rag-matryoshka
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1). It maps sentences & paragraphs to a 1024-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:** [mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) <!-- at revision 990580e27d329c7408b3741ecff85876e128e203 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 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': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, '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})
)
```
## 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("rbhatia46/mxbai-embed-large-v1-financial-rag-matryoshka")
# Run inference
sentences = [
'Microsoft, in their latest press release, revealed that they are anticipating a revenue growth of approximately 12% for the fiscal year ending in 2024.',
"What is Microsoft's projected revenue growth for fiscal year 2024?",
"What was the impact of COVID-19 on Zoom's profits?",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# 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_1024`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.8456 |
| cosine_accuracy@3 | 0.9392 |
| cosine_accuracy@5 | 0.9671 |
| cosine_accuracy@10 | 0.9899 |
| cosine_precision@1 | 0.8456 |
| cosine_precision@3 | 0.3131 |
| cosine_precision@5 | 0.1934 |
| cosine_precision@10 | 0.099 |
| cosine_recall@1 | 0.8456 |
| cosine_recall@3 | 0.9392 |
| cosine_recall@5 | 0.9671 |
| cosine_recall@10 | 0.9899 |
| cosine_ndcg@10 | 0.9212 |
| cosine_mrr@10 | 0.8989 |
| **cosine_map@100** | **0.8994** |
#### 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 | 0.8456 |
| cosine_accuracy@3 | 0.9392 |
| cosine_accuracy@5 | 0.9671 |
| cosine_accuracy@10 | 0.9899 |
| cosine_precision@1 | 0.8456 |
| cosine_precision@3 | 0.3131 |
| cosine_precision@5 | 0.1934 |
| cosine_precision@10 | 0.099 |
| cosine_recall@1 | 0.8456 |
| cosine_recall@3 | 0.9392 |
| cosine_recall@5 | 0.9671 |
| cosine_recall@10 | 0.9899 |
| cosine_ndcg@10 | 0.9217 |
| cosine_mrr@10 | 0.8995 |
| **cosine_map@100** | **0.8999** |
#### 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 | 0.8405 |
| cosine_accuracy@3 | 0.9367 |
| cosine_accuracy@5 | 0.9646 |
| cosine_accuracy@10 | 0.9899 |
| cosine_precision@1 | 0.8405 |
| cosine_precision@3 | 0.3122 |
| cosine_precision@5 | 0.1929 |
| cosine_precision@10 | 0.099 |
| cosine_recall@1 | 0.8405 |
| cosine_recall@3 | 0.9367 |
| cosine_recall@5 | 0.9646 |
| cosine_recall@10 | 0.9899 |
| cosine_ndcg@10 | 0.9186 |
| cosine_mrr@10 | 0.8955 |
| **cosine_map@100** | **0.8959** |
#### 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 | 0.8456 |
| cosine_accuracy@3 | 0.9392 |
| cosine_accuracy@5 | 0.9646 |
| cosine_accuracy@10 | 0.9899 |
| cosine_precision@1 | 0.8456 |
| cosine_precision@3 | 0.3131 |
| cosine_precision@5 | 0.1929 |
| cosine_precision@10 | 0.099 |
| cosine_recall@1 | 0.8456 |
| cosine_recall@3 | 0.9392 |
| cosine_recall@5 | 0.9646 |
| cosine_recall@10 | 0.9899 |
| cosine_ndcg@10 | 0.9201 |
| cosine_mrr@10 | 0.8976 |
| **cosine_map@100** | **0.898** |
#### 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 | 0.8405 |
| cosine_accuracy@3 | 0.9418 |
| cosine_accuracy@5 | 0.9646 |
| cosine_accuracy@10 | 0.9848 |
| cosine_precision@1 | 0.8405 |
| cosine_precision@3 | 0.3139 |
| cosine_precision@5 | 0.1929 |
| cosine_precision@10 | 0.0985 |
| cosine_recall@1 | 0.8405 |
| cosine_recall@3 | 0.9418 |
| cosine_recall@5 | 0.9646 |
| cosine_recall@10 | 0.9848 |
| cosine_ndcg@10 | 0.9171 |
| cosine_mrr@10 | 0.8949 |
| **cosine_map@100** | **0.8957** |
#### 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 | 0.8405 |
| cosine_accuracy@3 | 0.9316 |
| cosine_accuracy@5 | 0.957 |
| cosine_accuracy@10 | 0.9823 |
| cosine_precision@1 | 0.8405 |
| cosine_precision@3 | 0.3105 |
| cosine_precision@5 | 0.1914 |
| cosine_precision@10 | 0.0982 |
| cosine_recall@1 | 0.8405 |
| cosine_recall@3 | 0.9316 |
| cosine_recall@5 | 0.957 |
| cosine_recall@10 | 0.9823 |
| cosine_ndcg@10 | 0.9153 |
| cosine_mrr@10 | 0.8935 |
| **cosine_map@100** | **0.8943** |
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 3,550 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: 17 tokens</li><li>mean: 44.69 tokens</li><li>max: 105 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 18.26 tokens</li><li>max: 30 tokens</li></ul> |
* Samples:
| positive | anchor |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|
| <code>The total revenue for Google as of 2021 stands at approximately $181 billion, primarily driven by the performance of its advertising and cloud segments, hailing from the Information Technology sector.</code> | <code>What is the total revenue of Google as of 2021?</code> |
| <code>In Q4 2021, Amazon.com Inc. reported a significant increase in net income, reaching $14.3 billion, due to the surge in online shopping during the pandemic.</code> | <code>What was the Net Income of Amazon.com Inc. in Q4 2021?</code> |
| <code>Coca-Cola reported full-year 2021 revenue of $37.3 billion, a rise of 13% compared to $33.0 billion in 2020. This was primarily due to strong volume growth as well as improved pricing and mix.</code> | <code>How did Coca-Cola's revenue performance in 2021 measure against its previous year?</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
1024,
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
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`: 10
- `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`: 10
- `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_1024_cosine_map@100 | 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 |
|:----------:|:------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.8649 | 6 | - | 0.8783 | 0.8651 | 0.8713 | 0.8783 | 0.8439 | 0.8809 |
| 1.4414 | 10 | 0.7682 | - | - | - | - | - | - |
| 1.8739 | 13 | - | 0.8918 | 0.8827 | 0.8875 | 0.8918 | 0.8729 | 0.8933 |
| 2.8829 | 20 | 0.1465 | 0.8948 | 0.8896 | 0.8928 | 0.8961 | 0.8884 | 0.8953 |
| 3.8919 | 27 | - | 0.8930 | 0.8884 | 0.8917 | 0.8959 | 0.8900 | 0.8945 |
| 4.3243 | 30 | 0.0646 | - | - | - | - | - | - |
| 4.9009 | 34 | - | 0.8972 | 0.8883 | 0.8947 | 0.8955 | 0.8925 | 0.8970 |
| 5.7658 | 40 | 0.0397 | - | - | - | - | - | - |
| 5.9099 | 41 | - | 0.8964 | 0.8915 | 0.8953 | 0.8943 | 0.8926 | 0.8979 |
| 6.9189 | 48 | - | 0.8994 | 0.8930 | 0.8966 | 0.8955 | 0.8932 | 0.8974 |
| 7.2072 | 50 | 0.0319 | - | - | - | - | - | - |
| 7.9279 | 55 | - | 0.8998 | 0.8945 | 0.8967 | 0.8961 | 0.8943 | 0.8999 |
| **8.6486** | **60** | **0.0296** | **0.8994** | **0.8957** | **0.898** | **0.8959** | **0.8943** | **0.8999** |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.6
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.32.1
- 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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