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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-v1.5
widget:
- source_sentence: What is the anticipated total capital investment range for fiscal
year 2024 related to property and equipment?
sentences:
- Apollo, which we began offering as a commercial solution in 2021, is a cloud-agnostic,
single control layer that coordinates ongoing delivery of new features, security
updates, and platform configurations, helping to ensure the continuous operation
of critical systems.
- During fiscal year 2024, we expect to use our existing cash and cash equivalents,
our marketable securities, and the cash generated by our operations to fund our
capital investments of approximately $1.10 billion to $1.30 billion related to
property and equipment.
- Joseph F. Wayland was appointed as General Counsel and Secretary of Chubb Limited
in July 2013.
- source_sentence: What was the effective price per share of class A common stock
for fiscal 2023 under the U.S. retrospective responsibility plan?
sentences:
- We operated 692 gas stations at the end of 2023.
- The effective price per share for class A common stock under the U.S. retrospective
responsibility plan for fiscal 2023 was $221.33, calculated using the weighted-average
price based on the volume-weighted average price during the pricing period.
- For a description of our material pending legal proceedings, see Legal Matters
in Array 10 of the Notes to Consolidated Financial Statements included in Part
II, Item 8 of this Annual Report on...
- source_sentence: What financial challenge did the company face in 2017 and how did
it impact them legally?
sentences:
- In 2017, we experienced a material cybersecurity incident following a criminal
attack on our systems that involved the theft of personal information of consumers.
As a result of the 2017 cybersecurity incident, we were subject to proceedings
and investigations.
- The overall net effect on our gross margin from inventory provisions and sales
of items previously written down was an unfavorable impact of 7.5% in fiscal year
2023 and 0.9% in fiscal year 2022.
- The adjusted after-tax return on invested capital (ROIC) is computed by dividing
the after-tax operating profit, excluding rent expenses, by the total invested
capital which factors in the capitalization of operating leases.
- source_sentence: What is the title of Item 8 in the document?
sentences:
- Research and development expenses for fiscal year 2023 increased by $142 million
over the previous fiscal year.
- As of January 28, 2023, the company was authorized to issue up to 10,000,000 preferred
shares, each with a par value of $0.01. However, there were no preferred shares
issued and outstanding as of January 28, 2023 and the previous year as well.
- Item 8 of the document is titled 'Financial Statements and Supplementary Data'.
- source_sentence: How many shares were outstanding at the beginning of 2023 and what
was their aggregate intrinsic value?
sentences:
- At the beginning of 2023, there were 355 shares outstanding with an aggregate
intrinsic value of $142,916.
- In IBM’s 2023 Annual Report to Stockholders, the Financial Statements and Supplementary
Data are included on pages 44 through 121.
- Privacy and data protection regulations affect operations by dictating how data
is used and handled, impacting product offering and operation.
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: 0.7171428571428572
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8414285714285714
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.87
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9071428571428571
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7171428571428572
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.28047619047619043
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.174
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09071428571428569
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7171428571428572
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8414285714285714
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.87
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9071428571428571
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8148141512407042
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7849903628117916
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7887661106132665
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.7171428571428572
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8471428571428572
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8671428571428571
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9085714285714286
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7171428571428572
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.28238095238095234
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1734285714285714
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09085714285714284
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7171428571428572
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8471428571428572
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8671428571428571
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9085714285714286
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8146547679133024
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7843815192743763
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7879908541996482
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.7171428571428572
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8414285714285714
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8728571428571429
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9014285714285715
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7171428571428572
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.28047619047619043
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17457142857142854
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09014285714285714
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7171428571428572
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8414285714285714
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8728571428571429
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9014285714285715
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.81391710609103
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7854092970521545
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7894965954567308
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: 0.8242857142857143
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8585714285714285
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9028571428571428
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7128571428571429
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2747619047619047
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1717142857142857
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09028571428571427
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7128571428571429
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8242857142857143
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8585714285714285
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9028571428571428
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8071617730563218
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7765963718820862
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7803238233984962
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.6914285714285714
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7942857142857143
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8314285714285714
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8728571428571429
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6914285714285714
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.26476190476190475
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16628571428571426
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08728571428571427
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6914285714285714
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7942857142857143
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8314285714285714
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8728571428571429
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7805313652937041
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7512346938775507
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7553555070551027
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 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("phucvt0302/bge-base-financial-matryoshka")
# Run inference
sentences = [
'How many shares were outstanding at the beginning of 2023 and what was their aggregate intrinsic value?',
'At the beginning of 2023, there were 355 shares outstanding with an aggregate intrinsic value of $142,916.',
'In IBM’s 2023 Annual Report to Stockholders, the Financial Statements and Supplementary Data are included on pages 44 through 121.',
]
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) with these parameters:
```json
{
"truncate_dim": 768
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.7171 |
| cosine_accuracy@3 | 0.8414 |
| cosine_accuracy@5 | 0.87 |
| cosine_accuracy@10 | 0.9071 |
| cosine_precision@1 | 0.7171 |
| cosine_precision@3 | 0.2805 |
| cosine_precision@5 | 0.174 |
| cosine_precision@10 | 0.0907 |
| cosine_recall@1 | 0.7171 |
| cosine_recall@3 | 0.8414 |
| cosine_recall@5 | 0.87 |
| cosine_recall@10 | 0.9071 |
| **cosine_ndcg@10** | **0.8148** |
| cosine_mrr@10 | 0.785 |
| cosine_map@100 | 0.7888 |
#### 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 | 0.7171 |
| cosine_accuracy@3 | 0.8471 |
| cosine_accuracy@5 | 0.8671 |
| cosine_accuracy@10 | 0.9086 |
| cosine_precision@1 | 0.7171 |
| cosine_precision@3 | 0.2824 |
| cosine_precision@5 | 0.1734 |
| cosine_precision@10 | 0.0909 |
| cosine_recall@1 | 0.7171 |
| cosine_recall@3 | 0.8471 |
| cosine_recall@5 | 0.8671 |
| cosine_recall@10 | 0.9086 |
| **cosine_ndcg@10** | **0.8147** |
| cosine_mrr@10 | 0.7844 |
| cosine_map@100 | 0.788 |
#### 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 | 0.7171 |
| cosine_accuracy@3 | 0.8414 |
| cosine_accuracy@5 | 0.8729 |
| cosine_accuracy@10 | 0.9014 |
| cosine_precision@1 | 0.7171 |
| cosine_precision@3 | 0.2805 |
| cosine_precision@5 | 0.1746 |
| cosine_precision@10 | 0.0901 |
| cosine_recall@1 | 0.7171 |
| cosine_recall@3 | 0.8414 |
| cosine_recall@5 | 0.8729 |
| cosine_recall@10 | 0.9014 |
| **cosine_ndcg@10** | **0.8139** |
| cosine_mrr@10 | 0.7854 |
| cosine_map@100 | 0.7895 |
#### 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 | 0.7129 |
| cosine_accuracy@3 | 0.8243 |
| cosine_accuracy@5 | 0.8586 |
| cosine_accuracy@10 | 0.9029 |
| cosine_precision@1 | 0.7129 |
| cosine_precision@3 | 0.2748 |
| cosine_precision@5 | 0.1717 |
| cosine_precision@10 | 0.0903 |
| cosine_recall@1 | 0.7129 |
| cosine_recall@3 | 0.8243 |
| cosine_recall@5 | 0.8586 |
| cosine_recall@10 | 0.9029 |
| **cosine_ndcg@10** | **0.8072** |
| cosine_mrr@10 | 0.7766 |
| cosine_map@100 | 0.7803 |
#### 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 | 0.6914 |
| cosine_accuracy@3 | 0.7943 |
| cosine_accuracy@5 | 0.8314 |
| cosine_accuracy@10 | 0.8729 |
| cosine_precision@1 | 0.6914 |
| cosine_precision@3 | 0.2648 |
| cosine_precision@5 | 0.1663 |
| cosine_precision@10 | 0.0873 |
| cosine_recall@1 | 0.6914 |
| cosine_recall@3 | 0.7943 |
| cosine_recall@5 | 0.8314 |
| cosine_recall@10 | 0.8729 |
| **cosine_ndcg@10** | **0.7805** |
| cosine_mrr@10 | 0.7512 |
| cosine_map@100 | 0.7554 |
<!--
## 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.*
-->
## 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: 7 tokens</li><li>mean: 20.53 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 44.95 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| anchor | positive |
|:----------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What does the No Surprises Act require providers to develop and disclose?</code> | <code>Under the No Surprises Act, which went into effect January 1, 2022, certain providers, including DaVita, are required to develop and disclose a 'Good Faith Estimate' that details the expected charges for furnishing certain items or services.</code> |
| <code>What does Gross Merchandise Volume (GMV) represent in financial terms?</code> | <code>GMV consists of the total value of all paid transactions between users on our platforms during the applicable period inclusive of shipping fees and taxes.</code> |
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* 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
- `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
- `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
- `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, '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
- `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 |
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
| 0.9697 | 6 | - | 0.7993 | 0.7967 | 0.7941 | 0.7845 | 0.7431 |
| 1.6162 | 10 | 2.3395 | - | - | - | - | - |
| 1.9394 | 12 | - | 0.8089 | 0.8086 | 0.8108 | 0.8007 | 0.7669 |
| 2.9091 | 18 | - | 0.8158 | 0.8134 | 0.8144 | 0.8066 | 0.7761 |
| 3.2323 | 20 | 1.0419 | - | - | - | - | - |
| **3.8788** | **24** | **-** | **0.8148** | **0.8147** | **0.8139** | **0.8072** | **0.7805** |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.12.6
- Sentence Transformers: 4.1.0
- Transformers: 4.40.2
- PyTorch: 2.6.0+cu124
- Accelerate: 1.6.0
- 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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