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---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:6300
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: nomic-ai/nomic-embed-text-v1
widget:
- source_sentence: What amount of senior notes was repaid during fiscal 2022?
sentences:
- 'The following table sets forth the breakdown of revenue by geography, determined
based on the location of the Host’s listing (in millions): | Year Ended December
31, | 2021 | 2022 | 2023 United States | $ | 2,996 | | $ | 3,890 | $ | 4,290 International(1)
| 2,996 | | 4,509 | | 5,627 Total revenue | $ | 5,992 | | $ | 8,399 | $ | 9,917'
- During fiscal 2022, $2.25 billion of senior notes was repaid.
- Several factors are considered in developing the estimate for the long-term expected
rate of return on plan assets. For the defined benefit retirement plans, these
factors include historical rates of return of broad equity and bond indices and
projected long-term rates of return obtained from pension investment consultants.
The expected long-term rates of return for plan assets are 8 - 9% for equities
and 3 - 5% for bonds. For other retiree benefit plans, the expected long-term
rate of return reflects that the assets are comprised primarily of Company stock.
The expected rate of return on Company stock is based on the long-term projected
return of 8.5% and reflects the historical pattern of returns.
- source_sentence: What does GameStop Corp. offer to its customers?
sentences:
- State fraud and abuse laws could lead to criminal, civil, or administrative consequences,
including licensure loss, exclusion from healthcare programs, and significant
negative effects on the violating entity's business operations and financial health
if the laws are violated.
- GameStop Corp. offers games and entertainment products through its stores and
ecommerce platforms.
- Stribild is an oral formulation dosed once a day for the treatment of HIV-1 infection
in certain patients.
- source_sentence: How might a 10% change in the obsolescence reserve percentage impact
net earnings?
sentences:
- A 10% change in our obsolescence reserve percentage at January 28, 2023 would
have affected net earnings by approximately $2.5 million in fiscal 2022.
- The information required by Item 3 on Legal Proceedings is provided by referencing
Note 19 of the Notes to Consolidated Financial Statements in Item 8.
- ured notes for an aggregate principal amount of $18.50 billion. These notes were
issued in multiple series, which mature from 2027 through 2063.
- source_sentence: What are the SEC's regulations for security-based swap dealers
like Goldman Sachs' subsidiaries?
sentences:
- The increase in other income, net was primarily due to an increase in interest
income as a result of higher cash balances and higher interest rates.
- Through our Stubs loyalty programs, we have developed a consumer database of approximately
32 million households, representing approximately 64 million individuals.
- SEC rules govern the registration and regulation of security-based swap dealers.
Security-based swaps are defined as swaps on single securities, single loans or
narrow-based baskets or indices of securities. The SEC has adopted a number of
rules for security-based swap dealers, including (i) capital, margin and segregation
requirements; (ii) record-keeping, financial reporting and notification requirements;
(iii) business conduct standards; (iv) regulatory and public trade reporting;
and (v) the application of risk mitigation techniques to uncleared portfolios
of security-based swaps.
- source_sentence: How is the information about legal proceedings organized in the
financial documents according to the provided context?
sentences:
- The information about legal proceedings is organized under Part II, Item 8 in
the section titled 'Financial Statements and Supplementary Data – Note 14'.
- We have a match-funding policy that addresses the interest rate risk by aligning
the interest rate profile (fixed or floating rate and duration) of our debt portfolio
with the interest rate profile of our finance receivable portfolio within a predetermined
range on an ongoing basis. In connection with that policy, we use interest rate
derivative instruments to modify the debt structure to match assets within the
finance receivable portfolio.
- Achieved adjusted FIFO operating profit of $5.1 billion, which represents an 18%
increase compared to 2021.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
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
model-index:
- name: Nomic Financial Matryoshka
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.7457142857142857
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8614285714285714
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8957142857142857
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.93
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7457142857142857
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.28714285714285714
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1791428571428571
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09299999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7457142857142857
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8614285714285714
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8957142857142857
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.93
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8398915226132163
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8107896825396824
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8136819482601757
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.7357142857142858
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8514285714285714
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8914285714285715
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.93
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7357142857142858
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2838095238095238
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17828571428571427
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09299999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7357142857142858
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8514285714285714
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8914285714285715
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.93
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8352581932886503
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8047103174603173
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8075415578285141
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.7285714285714285
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8614285714285714
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8857142857142857
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9271428571428572
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7285714285714285
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.28714285714285714
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17714285714285713
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09271428571428571
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7285714285714285
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8614285714285714
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8857142857142857
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9271428571428572
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8319809230146766
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8011235827664398
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8040552556779361
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.7128571428571429
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8328571428571429
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8671428571428571
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9142857142857143
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7128571428571429
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2776190476190476
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1734285714285714
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09142857142857141
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7128571428571429
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8328571428571429
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8671428571428571
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9142857142857143
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8145627876253931
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7825572562358278
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7859620809117356
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.6642857142857143
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8042857142857143
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8457142857142858
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9028571428571428
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6642857142857143
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2680952380952381
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16914285714285712
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09028571428571427
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6642857142857143
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8042857142857143
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8457142857142858
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9028571428571428
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7821373629924483
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7436649659863942
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7468498882402747
name: Cosine Map@100
---
# Nomic Financial Matryoshka
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/nomic-embed-text-v1](https://huggingface.co/nomic-ai/nomic-embed-text-v1) 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:** [nomic-ai/nomic-embed-text-v1](https://huggingface.co/nomic-ai/nomic-embed-text-v1) <!-- at revision eb6b20cd65fcbdf7a2bc4ebac97908b3b21da981 -->
- **Maximum Sequence Length:** 8192 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': 8192, 'do_lower_case': False}) with Transformer model: NomicBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, '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("aniket0898/bge-base-financial-matryoshka")
# Run inference
sentences = [
'How is the information about legal proceedings organized in the financial documents according to the provided context?',
"The information about legal proceedings is organized under Part II, Item 8 in the section titled 'Financial Statements and Supplementary Data – Note 14'.",
'We have a match-funding policy that addresses the interest rate risk by aligning the interest rate profile (fixed or floating rate and duration) of our debt portfolio with the interest rate profile of our finance receivable portfolio within a predetermined range on an ongoing basis. In connection with that policy, we use interest rate derivative instruments to modify the debt structure to match assets within the finance receivable portfolio.',
]
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]
```
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</details>
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<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
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### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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## 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 | 0.7457 |
| cosine_accuracy@3 | 0.8614 |
| cosine_accuracy@5 | 0.8957 |
| cosine_accuracy@10 | 0.93 |
| cosine_precision@1 | 0.7457 |
| cosine_precision@3 | 0.2871 |
| cosine_precision@5 | 0.1791 |
| cosine_precision@10 | 0.093 |
| cosine_recall@1 | 0.7457 |
| cosine_recall@3 | 0.8614 |
| cosine_recall@5 | 0.8957 |
| cosine_recall@10 | 0.93 |
| cosine_ndcg@10 | 0.8399 |
| cosine_mrr@10 | 0.8108 |
| **cosine_map@100** | **0.8137** |
#### 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 | 0.7357 |
| cosine_accuracy@3 | 0.8514 |
| cosine_accuracy@5 | 0.8914 |
| cosine_accuracy@10 | 0.93 |
| cosine_precision@1 | 0.7357 |
| cosine_precision@3 | 0.2838 |
| cosine_precision@5 | 0.1783 |
| cosine_precision@10 | 0.093 |
| cosine_recall@1 | 0.7357 |
| cosine_recall@3 | 0.8514 |
| cosine_recall@5 | 0.8914 |
| cosine_recall@10 | 0.93 |
| cosine_ndcg@10 | 0.8353 |
| cosine_mrr@10 | 0.8047 |
| **cosine_map@100** | **0.8075** |
#### 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 | 0.7286 |
| cosine_accuracy@3 | 0.8614 |
| cosine_accuracy@5 | 0.8857 |
| cosine_accuracy@10 | 0.9271 |
| cosine_precision@1 | 0.7286 |
| cosine_precision@3 | 0.2871 |
| cosine_precision@5 | 0.1771 |
| cosine_precision@10 | 0.0927 |
| cosine_recall@1 | 0.7286 |
| cosine_recall@3 | 0.8614 |
| cosine_recall@5 | 0.8857 |
| cosine_recall@10 | 0.9271 |
| cosine_ndcg@10 | 0.832 |
| cosine_mrr@10 | 0.8011 |
| **cosine_map@100** | **0.8041** |
#### 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 | 0.7129 |
| cosine_accuracy@3 | 0.8329 |
| cosine_accuracy@5 | 0.8671 |
| cosine_accuracy@10 | 0.9143 |
| cosine_precision@1 | 0.7129 |
| cosine_precision@3 | 0.2776 |
| cosine_precision@5 | 0.1734 |
| cosine_precision@10 | 0.0914 |
| cosine_recall@1 | 0.7129 |
| cosine_recall@3 | 0.8329 |
| cosine_recall@5 | 0.8671 |
| cosine_recall@10 | 0.9143 |
| cosine_ndcg@10 | 0.8146 |
| cosine_mrr@10 | 0.7826 |
| **cosine_map@100** | **0.786** |
#### 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 | 0.6643 |
| cosine_accuracy@3 | 0.8043 |
| cosine_accuracy@5 | 0.8457 |
| cosine_accuracy@10 | 0.9029 |
| cosine_precision@1 | 0.6643 |
| cosine_precision@3 | 0.2681 |
| cosine_precision@5 | 0.1691 |
| cosine_precision@10 | 0.0903 |
| cosine_recall@1 | 0.6643 |
| cosine_recall@3 | 0.8043 |
| cosine_recall@5 | 0.8457 |
| cosine_recall@10 | 0.9029 |
| cosine_ndcg@10 | 0.7821 |
| cosine_mrr@10 | 0.7437 |
| **cosine_map@100** | **0.7468** |
<!--
## 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>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 2 tokens</li><li>mean: 20.47 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 45.09 tokens</li><li>max: 272 tokens</li></ul> |
* Samples:
| anchor | positive |
|:-------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What was the stored value of cards and loyalty program balances at the end of fiscal year 2022?</code> | <code>Stored value cards and loyalty program at October 2, 2022 showed a balance of approximately $1.503 billion.</code> |
| <code>What transformation is planned for Le Jardin located at The Londoner Macao?</code> | <code>Le Jardin, located on the southern flank of The Londoner Macao, is to undergo a transformation into a distinctive garden-themed attraction spanning approximately 50,000 square meters.</code> |
| <code>What are the key terms of the new Labor Agreement ratified by the UAW in 2023?</code> | <code>The key terms and provisions of the Labor Agreement are: General wage increases of 11% upon ratification in 2023, 3% in September each of 2024, 2025 and 2026, and 5% in September 2027; Consolidation of applicable wage classifications for in-progression, temporary and other employees – with employees reaching the top classification rate upon the completion of 156 weeks of active service; The re-establishment of a cost-of-living allowance; Lump sum ratification bonus payments of $5,000 paid to eligible employees in the three months ended December 31, 2023; For members currently employed and enrolled in the Employees’ Pension Plan, an increase of $5.00 to the monthly basic benefit for past and future service provided; A 3.6% increase in company contributions to eligible employees' defined contribution retirement accounts; and Annual contribution of $500 to eligible retirees or surviving spouses.</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_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 | 0.7331 | - | - | - | - | - |
| 0.9746 | 12 | - | 0.7871 | 0.7796 | 0.7747 | 0.7546 | 0.7214 |
| 1.6244 | 20 | 0.2506 | - | - | - | - | - |
| 1.9492 | 24 | - | 0.8021 | 0.7990 | 0.7869 | 0.7691 | 0.7371 |
| 2.4365 | 30 | 0.1029 | - | - | - | - | - |
| 2.9239 | 36 | - | 0.8030 | 0.8017 | 0.7926 | 0.7760 | 0.7402 |
| 3.2487 | 40 | 0.054 | - | - | - | - | - |
| **3.8985** | **48** | **-** | **0.8055** | **0.799** | **0.7924** | **0.7754** | **0.7383** |
| 0.8122 | 10 | 0.0397 | - | - | - | - | - |
| 0.9746 | 12 | - | 0.8109 | 0.7983 | 0.7974 | 0.7795 | 0.7373 |
| 1.6244 | 20 | 0.0301 | - | - | - | - | - |
| 1.9492 | 24 | - | 0.8115 | 0.8049 | 0.8026 | 0.7839 | 0.7486 |
| 2.4365 | 30 | 0.0236 | - | - | - | - | - |
| 2.9239 | 36 | - | 0.8138 | 0.8082 | 0.8045 | 0.7858 | 0.7470 |
| 3.2487 | 40 | 0.0131 | - | - | - | - | - |
| **3.8985** | **48** | **-** | **0.8137** | **0.8075** | **0.8041** | **0.786** | **0.7468** |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.8.10
- Sentence Transformers: 3.2.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 1.0.1
- 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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