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---
base_model: BAAI/bge-base-en-v1.5
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:6300
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Item 3—Legal Proceedings See discussion of Legal Proceedings in
Note 10 to the consolidated financial statements included in Item 8 of this Report.
sentences:
- What financial measures are presented on a non-GAAP basis in this Annual Report
on Form 10-K?
- Which section of the report discusses Legal Proceedings?
- What criteria was used to audit the internal control over financial reporting
of The Procter & Gamble Company as of June 30, 2023?
- source_sentence: A portion of the defense and/or settlement costs associated with
such litigation is covered by indemnification from third parties in limited cases.
sentences:
- How did the writers' and actors' strikes affect the Company's entertainment segment
in 2023?
- Can indemnification from third parties also contribute to covering litigation
costs?
- What was the balance of net cash used in financing activities for Costco for the
52 weeks ended August 28, 2022?
- source_sentence: In the company, to have a diverse and inclusive workforce, there
is an emphasis on attracting and hiring talented people who represent a mix of
backgrounds, identities, and experiences.
sentences:
- What does AT&T emphasize to ensure they have a diverse and inclusive workforce?
- What drove the growth in marketplace revenue for the year ended December 31, 2023?
- What was the effect of prior-period medical claims reserve development on the
Insurance segment's benefit ratio in 2023?
- source_sentence: Internal control over financial reporting is a process designed
to provide reasonable assurance regarding the reliability of financial reporting
and the preparation of financial statements for external purposes in accordance
with generally accepted accounting principles. It includes various policies and
procedures that ensure accurate and fair record maintenance, proper transaction
recording, and prevention or detection of unauthorized use or acquisition of assets.
sentences:
- How much did net cash used in financing activities decrease in fiscal 2023 compared
to the previous fiscal year?
- How does Visa ensure the protection of its intellectual property?
- What is the purpose of internal control over financial reporting according to
the document?
- source_sentence: Non-GAAP earnings from operations and non-GAAP operating profit
margin consist of earnings from operations or earnings from operations as a percentage
of net revenue excluding the items mentioned above and charges relating to the
amortization of intangible assets, goodwill impairment, transformation costs and
acquisition, disposition and other related charges. Hewlett Packard Enterprise
excludes these items because they are non-cash expenses, are significantly impacted
by the timing and magnitude of acquisitions, and are inconsistent in amount and
frequency.
sentences:
- What specific charges are excluded from Hewlett Packard Enterprise's non-GAAP
operating profit margin and why?
- How many shares were outstanding at the beginning of 2023 and what was their aggregate
intrinsic value?
- What was the annual amortization expense forecast for acquisition-related intangible
assets in 2025, according to a specified financial projection?
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: 0.7157142857142857
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8571428571428571
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8871428571428571
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9314285714285714
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7157142857142857
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2857142857142857
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1774285714285714
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09314285714285712
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7157142857142857
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8571428571428571
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8871428571428571
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9314285714285714
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8274896625809096
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7939818594104311
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7969204030602811
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.7142857142857143
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8571428571428571
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8871428571428571
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9314285714285714
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7142857142857143
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2857142857142857
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1774285714285714
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09314285714285712
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7142857142857143
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8571428571428571
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8871428571428571
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9314285714285714
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8267670378473014
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7930204081632654
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7958033409607879
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.7157142857142857
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8514285714285714
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8828571428571429
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.93
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7157142857142857
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2838095238095238
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17657142857142857
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09299999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7157142857142857
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8514285714285714
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8828571428571429
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.93
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.825504930245723
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7918724489795919
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7945830508495424
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.7142857142857143
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8428571428571429
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8742857142857143
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9214285714285714
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7142857142857143
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.28095238095238095
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17485714285714282
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09214285714285712
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7142857142857143
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8428571428571429
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8742857142857143
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9214285714285714
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8203162516614704
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7878543083900227
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7909435994513387
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.6828571428571428
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.81
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.85
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9042857142857142
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6828571428571428
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.27
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16999999999999998
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09042857142857143
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6828571428571428
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.81
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.85
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9042857142857142
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7926026006937184
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7570844671201811
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7606949750229449
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). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** 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': 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("NickyNicky/bge-base-financial-matryoshka")
# Run inference
sentences = [
'Non-GAAP earnings from operations and non-GAAP operating profit margin consist of earnings from operations or earnings from operations as a percentage of net revenue excluding the items mentioned above and charges relating to the amortization of intangible assets, goodwill impairment, transformation costs and acquisition, disposition and other related charges. Hewlett Packard Enterprise excludes these items because they are non-cash expenses, are significantly impacted by the timing and magnitude of acquisitions, and are inconsistent in amount and frequency.',
"What specific charges are excluded from Hewlett Packard Enterprise's non-GAAP operating profit margin and why?",
'How many shares were outstanding at the beginning of 2023 and what was their aggregate intrinsic value?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Information Retrieval
* Dataset: `dim_768`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.7157 |
| cosine_accuracy@3 | 0.8571 |
| cosine_accuracy@5 | 0.8871 |
| cosine_accuracy@10 | 0.9314 |
| cosine_precision@1 | 0.7157 |
| cosine_precision@3 | 0.2857 |
| cosine_precision@5 | 0.1774 |
| cosine_precision@10 | 0.0931 |
| cosine_recall@1 | 0.7157 |
| cosine_recall@3 | 0.8571 |
| cosine_recall@5 | 0.8871 |
| cosine_recall@10 | 0.9314 |
| cosine_ndcg@10 | 0.8275 |
| cosine_mrr@10 | 0.794 |
| **cosine_map@100** | **0.7969** |
#### 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 | 0.7143 |
| cosine_accuracy@3 | 0.8571 |
| cosine_accuracy@5 | 0.8871 |
| cosine_accuracy@10 | 0.9314 |
| cosine_precision@1 | 0.7143 |
| cosine_precision@3 | 0.2857 |
| cosine_precision@5 | 0.1774 |
| cosine_precision@10 | 0.0931 |
| cosine_recall@1 | 0.7143 |
| cosine_recall@3 | 0.8571 |
| cosine_recall@5 | 0.8871 |
| cosine_recall@10 | 0.9314 |
| cosine_ndcg@10 | 0.8268 |
| cosine_mrr@10 | 0.793 |
| **cosine_map@100** | **0.7958** |
#### 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 | 0.7157 |
| cosine_accuracy@3 | 0.8514 |
| cosine_accuracy@5 | 0.8829 |
| cosine_accuracy@10 | 0.93 |
| cosine_precision@1 | 0.7157 |
| cosine_precision@3 | 0.2838 |
| cosine_precision@5 | 0.1766 |
| cosine_precision@10 | 0.093 |
| cosine_recall@1 | 0.7157 |
| cosine_recall@3 | 0.8514 |
| cosine_recall@5 | 0.8829 |
| cosine_recall@10 | 0.93 |
| cosine_ndcg@10 | 0.8255 |
| cosine_mrr@10 | 0.7919 |
| **cosine_map@100** | **0.7946** |
#### 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 | 0.7143 |
| cosine_accuracy@3 | 0.8429 |
| cosine_accuracy@5 | 0.8743 |
| cosine_accuracy@10 | 0.9214 |
| cosine_precision@1 | 0.7143 |
| cosine_precision@3 | 0.281 |
| cosine_precision@5 | 0.1749 |
| cosine_precision@10 | 0.0921 |
| cosine_recall@1 | 0.7143 |
| cosine_recall@3 | 0.8429 |
| cosine_recall@5 | 0.8743 |
| cosine_recall@10 | 0.9214 |
| cosine_ndcg@10 | 0.8203 |
| cosine_mrr@10 | 0.7879 |
| **cosine_map@100** | **0.7909** |
#### 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 | 0.6829 |
| cosine_accuracy@3 | 0.81 |
| cosine_accuracy@5 | 0.85 |
| cosine_accuracy@10 | 0.9043 |
| cosine_precision@1 | 0.6829 |
| cosine_precision@3 | 0.27 |
| cosine_precision@5 | 0.17 |
| cosine_precision@10 | 0.0904 |
| cosine_recall@1 | 0.6829 |
| cosine_recall@3 | 0.81 |
| cosine_recall@5 | 0.85 |
| cosine_recall@10 | 0.9043 |
| cosine_ndcg@10 | 0.7926 |
| cosine_mrr@10 | 0.7571 |
| **cosine_map@100** | **0.7607** |
<!--
## 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.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* 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: 46.8 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 20.89 tokens</li><li>max: 51 tokens</li></ul> |
* Samples:
| positive | anchor |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------|
| <code>Retail sales mix by product type for company-operated stores shows beverages at 74%, food at 22%, and other items at 4%.</code> | <code>What are the primary products sold in Starbucks company-operated stores?</code> |
| <code>The pre-tax adjustment for transformation costs was $136 in 2021 and $111 in 2020. Transformation costs primarily include costs related to store and business closure costs and third party professional consulting fees associated with business transformation and cost saving initiatives.</code> | <code>What was the purpose of pre-tax adjustments for transformation costs by The Kroger Co.?</code> |
| <code>HP's Consolidated Financial Statements are prepared in accordance with United States generally accepted accounting principles (GAAP).</code> | <code>What principles do HP's Consolidated Financial Statements adhere to?</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`: 40
- `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
- `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`: 40
- `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`: False
- `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_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 |
|:------:|:----:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.9114 | 9 | - | 0.7311 | 0.7527 | 0.7618 | 0.6911 | 0.7612 |
| 1.0127 | 10 | 1.9734 | - | - | - | - | - |
| 1.9241 | 19 | - | 0.7638 | 0.7748 | 0.7800 | 0.7412 | 0.7836 |
| 2.0253 | 20 | 0.8479 | - | - | - | - | - |
| 2.9367 | 29 | - | 0.7775 | 0.7842 | 0.7902 | 0.7473 | 0.7912 |
| 3.0380 | 30 | 0.524 | - | - | - | - | - |
| 3.9494 | 39 | - | 0.7831 | 0.7860 | 0.7915 | 0.7556 | 0.7939 |
| 4.0506 | 40 | 0.3826 | - | - | - | - | - |
| 4.9620 | 49 | - | 0.7896 | 0.7915 | 0.7927 | 0.7616 | 0.7983 |
| 5.0633 | 50 | 0.3165 | - | - | - | - | - |
| 5.9747 | 59 | - | 0.7925 | 0.7946 | 0.7943 | 0.7603 | 0.7978 |
| 6.0759 | 60 | 0.2599 | - | - | - | - | - |
| 6.9873 | 69 | - | 0.7918 | 0.7949 | 0.7951 | 0.7608 | 0.7976 |
| 7.0886 | 70 | 0.2424 | - | - | - | - | - |
| 8.0 | 79 | - | 0.7925 | 0.7956 | 0.7959 | 0.7612 | 0.7989 |
| 8.1013 | 80 | 0.2243 | - | - | - | - | - |
| 8.9114 | 88 | - | 0.7927 | 0.7956 | 0.7961 | 0.7610 | 0.7983 |
| 9.1139 | 90 | 0.2222 | 0.7909 | 0.7946 | 0.7958 | 0.7607 | 0.7969 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.2.0+cu121
- Accelerate: 0.31.0
- 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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