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---
language:
- en
license: apache-2.0
library_name: sentence-transformers
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
datasets: []
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
widget:
- source_sentence: 'Forward-looking statements may appear throughout this report,
including without limitation, the following sections: “Management''s Discussion
and Analysis,” “Risk Factors” and "Notes 4, 8 and 13 to the Consolidated Financial
Statements."'
sentences:
- How does a one-year adjustment in the 2023 expected retirement age for U.S. plans
affect income before income taxes?
- Which sections of the report might contain forward-looking statements according
to the text?
- What was the allowance for loan and lease losses at Bank of America as of December
31, 2022?
- source_sentence: Interest income | $ | 267 | | | $ | 29 | | $ | 238 | | 821 | %
sentences:
- What are the key risks and uncertainties mentioned that could impact the validity
of DaVita's forward-looking statements?
- How did the interest income change in fiscal year 2023 compared to the previous
year?
- What are some of the main competitive factors in the interactive entertainment
industry?
- source_sentence: Veklury received U.S. Food and Drug Administration (FDA) and European
Commission (EC) approval to treat COVID-19 in patients with mild to severe hepatic
impairment and those with severe renal impairment, including those on dialysis.
sentences:
- What significant regulatory approvals did Gilead's Veklury receive?
- What type of information is included under the caption "Legal Proceedings" in
an Annual Report on Form 10-K?
- What was the cash change related to changes in operating assets and liabilities,
including working capital, in 2022?
- source_sentence: The net value of property, plant, and equipment for the consolidated
group increased from $12,028 million in 2022 to $12,680 million in 2023.
sentences:
- What steps does the company plan to take next after discussing data with regulators
and key opinion leaders?
- How does the company manage fluctuations in foreign currency exchange rates?
- What was the increase in property, plant, and equipment net value from 2022 to
2023 for the consolidated group?
- source_sentence: The effective duration of our total AFS and HTM investments securities
as of December 31, 2023 is approximately 3.9 years.
sentences:
- What are the effective durations of the total Available-for-Sale (AFS) and Held-to-Maturity
(HTM) investment securities as of December 31, 2023?
- What was the net unit growth percentage for Hilton in the year ended December
31, 2023?
- What does goodwill represent in accounting?
pipeline_tag: sentence-similarity
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: 0.7285714285714285
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8485714285714285
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8885714285714286
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9214285714285714
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7285714285714285
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.28285714285714286
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17771428571428569
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09214285714285712
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7285714285714285
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8485714285714285
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8885714285714286
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9214285714285714
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8274202252845575
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7969903628117911
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7998523047098398
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.72
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8442857142857143
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8785714285714286
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.92
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.72
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2814285714285714
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17571428571428568
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09199999999999998
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.72
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8442857142857143
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8785714285714286
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.92
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8213589464095679
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7896825396825394
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7926726035572866
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.7214285714285714
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8385714285714285
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8742857142857143
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9128571428571428
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7214285714285714
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.27952380952380956
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17485714285714282
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09128571428571428
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7214285714285714
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8385714285714285
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8742857142857143
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9128571428571428
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8190844047519252
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7888673469387758
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7921199469128796
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.6971428571428572
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: 0.9057142857142857
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6971428571428572
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: 0.09057142857142855
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6971428571428572
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: 0.9057142857142857
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8054254319689889
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7729421768707481
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.776216648701894
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.6614285714285715
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7985714285714286
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8442857142857143
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8814285714285715
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6614285714285715
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.26619047619047614
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16885714285714284
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08814285714285712
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6614285714285715
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7985714285714286
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8442857142857143
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8814285714285715
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7728992637054746
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.737815759637188
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7417951294330247
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("Hritikmore/bge-base-financial-matryoshka")
# Run inference
sentences = [
'The effective duration of our total AFS and HTM investments securities as of December 31, 2023 is approximately 3.9 years.',
'What are the effective durations of the total Available-for-Sale (AFS) and Held-to-Maturity (HTM) investment securities as of December 31, 2023?',
'What was the net unit growth percentage for Hilton in the year ended December 31, 2023?',
]
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 | 0.7286 |
| cosine_accuracy@3 | 0.8486 |
| cosine_accuracy@5 | 0.8886 |
| cosine_accuracy@10 | 0.9214 |
| cosine_precision@1 | 0.7286 |
| cosine_precision@3 | 0.2829 |
| cosine_precision@5 | 0.1777 |
| cosine_precision@10 | 0.0921 |
| cosine_recall@1 | 0.7286 |
| cosine_recall@3 | 0.8486 |
| cosine_recall@5 | 0.8886 |
| cosine_recall@10 | 0.9214 |
| cosine_ndcg@10 | 0.8274 |
| cosine_mrr@10 | 0.797 |
| **cosine_map@100** | **0.7999** |
#### 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 | 0.72 |
| cosine_accuracy@3 | 0.8443 |
| cosine_accuracy@5 | 0.8786 |
| cosine_accuracy@10 | 0.92 |
| cosine_precision@1 | 0.72 |
| cosine_precision@3 | 0.2814 |
| cosine_precision@5 | 0.1757 |
| cosine_precision@10 | 0.092 |
| cosine_recall@1 | 0.72 |
| cosine_recall@3 | 0.8443 |
| cosine_recall@5 | 0.8786 |
| cosine_recall@10 | 0.92 |
| cosine_ndcg@10 | 0.8214 |
| cosine_mrr@10 | 0.7897 |
| **cosine_map@100** | **0.7927** |
#### 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 | 0.7214 |
| cosine_accuracy@3 | 0.8386 |
| cosine_accuracy@5 | 0.8743 |
| cosine_accuracy@10 | 0.9129 |
| cosine_precision@1 | 0.7214 |
| cosine_precision@3 | 0.2795 |
| cosine_precision@5 | 0.1749 |
| cosine_precision@10 | 0.0913 |
| cosine_recall@1 | 0.7214 |
| cosine_recall@3 | 0.8386 |
| cosine_recall@5 | 0.8743 |
| cosine_recall@10 | 0.9129 |
| cosine_ndcg@10 | 0.8191 |
| cosine_mrr@10 | 0.7889 |
| **cosine_map@100** | **0.7921** |
#### 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 | 0.6971 |
| cosine_accuracy@3 | 0.8329 |
| cosine_accuracy@5 | 0.8671 |
| cosine_accuracy@10 | 0.9057 |
| cosine_precision@1 | 0.6971 |
| cosine_precision@3 | 0.2776 |
| cosine_precision@5 | 0.1734 |
| cosine_precision@10 | 0.0906 |
| cosine_recall@1 | 0.6971 |
| cosine_recall@3 | 0.8329 |
| cosine_recall@5 | 0.8671 |
| cosine_recall@10 | 0.9057 |
| cosine_ndcg@10 | 0.8054 |
| cosine_mrr@10 | 0.7729 |
| **cosine_map@100** | **0.7762** |
#### 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 | 0.6614 |
| cosine_accuracy@3 | 0.7986 |
| cosine_accuracy@5 | 0.8443 |
| cosine_accuracy@10 | 0.8814 |
| cosine_precision@1 | 0.6614 |
| cosine_precision@3 | 0.2662 |
| cosine_precision@5 | 0.1689 |
| cosine_precision@10 | 0.0881 |
| cosine_recall@1 | 0.6614 |
| cosine_recall@3 | 0.7986 |
| cosine_recall@5 | 0.8443 |
| cosine_recall@10 | 0.8814 |
| cosine_ndcg@10 | 0.7729 |
| cosine_mrr@10 | 0.7378 |
| **cosine_map@100** | **0.7418** |
<!--
## 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: 2 tokens</li><li>mean: 45.87 tokens</li><li>max: 272 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 20.43 tokens</li><li>max: 41 tokens</li></ul> |
* Samples:
| positive | anchor |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------|
| <code>Significant judgment is required in evaluating our tax positions and during the ordinary course of business, there are many transactions and calculations for which the ultimate tax settlement is uncertain. As a result, we recognize the effect of this uncertainty on our tax attributes or taxes payable based on our estimates of the eventual outcome.</code> | <code>Why might the company's tax settlements vary?</code> |
| <code>OPSUMIT is used for the treatment of pediatric pulmonary arterial hypertension.</code> | <code>What medical condition does OPSUMIT treat?</code> |
| <code>Tangible equity ratios and tangible book value per share of common stock are non-GAAP financial measures. For more information on these ratios and corresponding reconciliations to GAAP financial measures, see Supplemental Financial Data and Non-GAAP Reconciliations.</code> | <code>What is the tangible equity ratio considered according to standard financial measures?</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
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 2
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `tf32`: False
- `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`: 8
- `per_device_eval_batch_size`: 8
- `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`: 2
- `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`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: False
- `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_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 |
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.2030 | 10 | 0.7168 | - | - | - | - | - |
| 0.4061 | 20 | 0.3345 | - | - | - | - | - |
| 0.6091 | 30 | 0.2234 | - | - | - | - | - |
| 0.8122 | 40 | 0.2126 | - | - | - | - | - |
| **0.9949** | **49** | **-** | **0.7796** | **0.7844** | **0.7905** | **0.7293** | **0.7973** |
| 1.0152 | 50 | 0.2301 | - | - | - | - | - |
| 1.2183 | 60 | 0.1595 | - | - | - | - | - |
| 1.4213 | 70 | 0.1082 | - | - | - | - | - |
| 1.6244 | 80 | 0.0911 | - | - | - | - | - |
| 1.8274 | 90 | 0.1068 | - | - | - | - | - |
| 1.9898 | 98 | - | 0.7762 | 0.7921 | 0.7927 | 0.7418 | 0.7999 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+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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