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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: BAAI/bge-base-en
widget:
- source_sentence: 'Employee health, safety and wellness are top priorities at Hasbro.
We support our colleagues’ well-being, which includes mental, physical and financial
wellness, through a number of programs, including: robust employee assistance
programs, childcare solutions, and a commitment to flexible work arrangements.'
sentences:
- What percentage of the total annual net trade sales did the sales returns reserve
represent for the company during each of the fiscal years 2023, 2022, and 2021?
- How does Hasbro support the wellness of its employees?
- What was the conclusion of the Company's review regarding the impact of the American
Rescue Plan, the Consolidated Appropriations Act, 2021, and related tax provisions
on its business for the fiscal year ended June 30, 2023?
- source_sentence: The Company has a minority market share in the global smartphone,
personal computer and tablet markets. The Company faces substantial competition
in these markets from companies that have significant technical, marketing, distribution
and other resources, as well as established hardware, software and digital content
supplier relationships. In addition, some of the Company’s competitors have broader
product lines, lower-priced products and a larger installed base of active devices.
Competition has been particularly intense as competitors have aggressively cut
prices and lowered product margins.
sentences:
- When did The Hershey Company declare the dividend that was paid on March 15, 2023?
- What factors contribute to the Company facing substantial competition in the markets
for smartphones, personal computers, and tablets?
- How is goodwill impairment analyzed?
- source_sentence: During fiscal 2022, there were cash payments of $6.7 billion for
repurchases of common stock through open market purchases.
sentences:
- What was the value of cash payments for common stock repurchases through open
market purchases during fiscal 2022?
- How much did the Compute & Networking segment's gross margin decrease in fiscal
year 2023?
- What different methods does Amazon use to engage and retain employees?
- source_sentence: Walmart Luminate provides a suite of data products for merchants
and suppliers.
sentences:
- What pages do the Consolidated Financial Statements and their accompanying Notes
and reports appear on in the document?
- What was the percentage change in NYSE total cash handled volume from 2022 to
2023?
- What is the function of Walmart Luminate?
- source_sentence: Item 8. Financial Statements and Supplementary Data. The Consolidated
Financial Statements, together with the Notes thereto and the report thereon dated
February 16, 2024, of PricewaterhouseCoopers LLP, the Firm’s independent registered
public accounting firm (PCAOB ID 238).
sentences:
- What type of data does Item 8 in a financial document contain?
- How did the assumptions and estimates used for assessing the fair value of reporting
units potentially impact the company's financial statements?
- What factors are considered when making estimates for financial statements?
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: 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: 0.20411392405063292
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.39082278481012656
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.45569620253164556
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5427215189873418
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.20411392405063292
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.1302742616033755
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.0911392405063291
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.054272151898734175
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.20411392405063292
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.39082278481012656
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.45569620253164556
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5427215189873418
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.3712962481916349
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.31667482921438606
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.32569334518419213
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.1787974683544304
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.38449367088607594
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.44936708860759494
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5221518987341772
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.1787974683544304
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.1281645569620253
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.08987341772151898
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05221518987341772
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.1787974683544304
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.38449367088607594
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.44936708860759494
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5221518987341772
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.35214780800723905
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.2974972372915411
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.30719274754259535
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.17563291139240506
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.33860759493670883
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.3924050632911392
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.49683544303797467
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.17563291139240506
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.11286919831223628
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.07848101265822786
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.04968354430379747
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.17563291139240506
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.33860759493670883
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.3924050632911392
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.49683544303797467
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.32777016757909155
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.2748675155716295
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.2839854758498125
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.13449367088607594
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.27689873417721517
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.34335443037974683
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.40189873417721517
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.13449367088607594
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.09229957805907173
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.06867088607594937
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.04018987341772152
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.13449367088607594
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.27689873417721517
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.34335443037974683
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.40189873417721517
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2642535058721437
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.2206462226240707
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.2315340997045677
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.08544303797468354
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.19462025316455697
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.24841772151898733
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.31645569620253167
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.08544303797468354
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.06487341772151899
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.04968354430379747
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.031645569620253174
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.08544303797468354
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.19462025316455697
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.24841772151898733
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.31645569620253167
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.19364593797751115
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.15531381856540089
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.16408720453627956
name: Cosine Map@100
---
# BGE base Financial Matryoshka
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) 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](https://huggingface.co/BAAI/bge-base-en) <!-- at revision b737bf5dcc6ee8bdc530531266b4804a5d77b5d8 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **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("RK-1235/bge-base-FIR-matryoshka-BASELINE-10epochs-FT")
# Run inference
sentences = [
'Item 8. Financial Statements and Supplementary Data. The Consolidated Financial Statements, together with the Notes thereto and the report thereon dated February 16, 2024, of PricewaterhouseCoopers LLP, the Firm’s independent registered public accounting firm (PCAOB ID 238).',
'What type of data does Item 8 in a financial document contain?',
"How did the assumptions and estimates used for assessing the fair value of reporting units potentially impact the company's financial statements?",
]
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)
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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) with these parameters:
```json
{
"truncate_dim": 768
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.2041 |
| cosine_accuracy@3 | 0.3908 |
| cosine_accuracy@5 | 0.4557 |
| cosine_accuracy@10 | 0.5427 |
| cosine_precision@1 | 0.2041 |
| cosine_precision@3 | 0.1303 |
| cosine_precision@5 | 0.0911 |
| cosine_precision@10 | 0.0543 |
| cosine_recall@1 | 0.2041 |
| cosine_recall@3 | 0.3908 |
| cosine_recall@5 | 0.4557 |
| cosine_recall@10 | 0.5427 |
| **cosine_ndcg@10** | **0.3713** |
| cosine_mrr@10 | 0.3167 |
| cosine_map@100 | 0.3257 |
#### Information Retrieval
* Dataset: `dim_512`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 512
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.1788 |
| cosine_accuracy@3 | 0.3845 |
| cosine_accuracy@5 | 0.4494 |
| cosine_accuracy@10 | 0.5222 |
| cosine_precision@1 | 0.1788 |
| cosine_precision@3 | 0.1282 |
| cosine_precision@5 | 0.0899 |
| cosine_precision@10 | 0.0522 |
| cosine_recall@1 | 0.1788 |
| cosine_recall@3 | 0.3845 |
| cosine_recall@5 | 0.4494 |
| cosine_recall@10 | 0.5222 |
| **cosine_ndcg@10** | **0.3521** |
| cosine_mrr@10 | 0.2975 |
| cosine_map@100 | 0.3072 |
#### Information Retrieval
* Dataset: `dim_256`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 256
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.1756 |
| cosine_accuracy@3 | 0.3386 |
| cosine_accuracy@5 | 0.3924 |
| cosine_accuracy@10 | 0.4968 |
| cosine_precision@1 | 0.1756 |
| cosine_precision@3 | 0.1129 |
| cosine_precision@5 | 0.0785 |
| cosine_precision@10 | 0.0497 |
| cosine_recall@1 | 0.1756 |
| cosine_recall@3 | 0.3386 |
| cosine_recall@5 | 0.3924 |
| cosine_recall@10 | 0.4968 |
| **cosine_ndcg@10** | **0.3278** |
| cosine_mrr@10 | 0.2749 |
| cosine_map@100 | 0.284 |
#### Information Retrieval
* Dataset: `dim_128`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 128
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.1345 |
| cosine_accuracy@3 | 0.2769 |
| cosine_accuracy@5 | 0.3434 |
| cosine_accuracy@10 | 0.4019 |
| cosine_precision@1 | 0.1345 |
| cosine_precision@3 | 0.0923 |
| cosine_precision@5 | 0.0687 |
| cosine_precision@10 | 0.0402 |
| cosine_recall@1 | 0.1345 |
| cosine_recall@3 | 0.2769 |
| cosine_recall@5 | 0.3434 |
| cosine_recall@10 | 0.4019 |
| **cosine_ndcg@10** | **0.2643** |
| cosine_mrr@10 | 0.2206 |
| cosine_map@100 | 0.2315 |
#### Information Retrieval
* Dataset: `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 64
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.0854 |
| cosine_accuracy@3 | 0.1946 |
| cosine_accuracy@5 | 0.2484 |
| cosine_accuracy@10 | 0.3165 |
| cosine_precision@1 | 0.0854 |
| cosine_precision@3 | 0.0649 |
| cosine_precision@5 | 0.0497 |
| cosine_precision@10 | 0.0316 |
| cosine_recall@1 | 0.0854 |
| cosine_recall@3 | 0.1946 |
| cosine_recall@5 | 0.2484 |
| cosine_recall@10 | 0.3165 |
| **cosine_ndcg@10** | **0.1936** |
| cosine_mrr@10 | 0.1553 |
| cosine_map@100 | 0.1641 |
<!--
## 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: 46.06 tokens</li><li>max: 371 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 20.8 tokens</li><li>max: 51 tokens</li></ul> |
* Samples:
| positive | anchor |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------|
| <code>As of December 31, 2023, a 5 percent change in the contingent consideration liabilities would result in a change in income before income taxes of $5.2 million.</code> | <code>How would a 5% change in the contingent consideration liabilities impact income before taxes as of December 31, 2023?</code> |
| <code>NIKE, Inc.'s principal business activity involves the design, development, and worldwide marketing and selling of athletic footwear, apparel, equipment, accessories, and services.</code> | <code>What is the principal business activity of NIKE, Inc.?</code> |
| <code>During 2023, changes in foreign currencies relative to the U.S. dollar negatively impacted net sales by approximately $3,484, 156 basis points, compared to 2022, attributable to our Canadian and Other International operations.</code> | <code>What was the overall impact of foreign currencies on net sales in 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`: 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
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 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`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `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
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 |
|:-------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
| 0.8122 | 10 | 89.0763 | - | - | - | - | - |
| **1.0** | **13** | **-** | **0.4022** | **0.3835** | **0.3505** | **0.2911** | **0.1835** |
| 1.5685 | 20 | 36.7538 | - | - | - | - | - |
| 2.0 | 26 | - | 0.3725 | 0.3591 | 0.3218 | 0.2753 | 0.1978 |
| 2.3249 | 30 | 17.7869 | - | - | - | - | - |
| 3.0 | 39 | - | 0.3680 | 0.3558 | 0.3284 | 0.2638 | 0.2000 |
| 3.0812 | 40 | 10.5904 | - | - | - | - | - |
| 3.8934 | 50 | 7.9568 | - | - | - | - | - |
| 4.0 | 52 | - | 0.3634 | 0.3487 | 0.3245 | 0.2589 | 0.1999 |
| 4.6497 | 60 | 5.5002 | - | - | - | - | - |
| 5.0 | 65 | - | 0.3648 | 0.3551 | 0.3211 | 0.2595 | 0.1968 |
| 5.4061 | 70 | 5.3314 | - | - | - | - | - |
| 6.0 | 78 | - | 0.3693 | 0.3548 | 0.3257 | 0.2621 | 0.1977 |
| 6.1624 | 80 | 4.6165 | - | - | - | - | - |
| 6.9746 | 90 | 4.7811 | - | - | - | - | - |
| 7.0 | 91 | - | 0.3698 | 0.3532 | 0.3293 | 0.2637 | 0.1954 |
| 7.7310 | 100 | 3.978 | - | - | - | - | - |
| 8.0 | 104 | - | 0.3713 | 0.3523 | 0.3273 | 0.2637 | 0.1952 |
| 8.4873 | 110 | 4.1624 | - | - | - | - | - |
| 9.0 | 117 | - | 0.3707 | 0.3517 | 0.3264 | 0.2639 | 0.1949 |
| 9.2437 | 120 | 3.4956 | - | - | - | - | - |
| 10.0 | 130 | 3.9661 | 0.3713 | 0.3521 | 0.3278 | 0.2643 | 0.1936 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 4.1.0
- Transformers: 4.52.2
- PyTorch: 2.6.0+cu124
- Accelerate: 1.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## 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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