---
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: For example, brands based on major motion picture releases generally
require less advertising as a result of the promotional activities around the
motion picture release.
sentences:
- How many new hotels did Hilton open in the year ended December 31, 2023?
- What impact do major motion picture releases have on Hasbro's advertising expenditures?
- In which item of the report can the details of Legal proceedings be found by referencing?
- source_sentence: Our retail stores are generally located in strip centers, shopping
malls and pedestrian areas... We target strip centers that are conveniently located,
have a mass merchant or supermarket anchor tenant and have a high volume of customers.
sentences:
- What is the range of pages in IBM’s 2023 Annual Report to Stockholders where the
Financial Statements and Supplementary Data are located?
- How does GameStop's store location choice impact its business strategy?
- In which part of the financial documents can detailed information about legal
proceedings be found according to Item 3?
- source_sentence: The increase in fulfillment costs in absolute dollars in 2023,
compared to the prior year, is primarily due to increased sales and investments
in our fulfillment network, partially offset by fulfillment network efficiencies.
sentences:
- What led to the increase in fulfillment costs in 2023?
- Where in the Form 10-K can one find Note 15 which discusses legal proceedings?
- What accounting method does the company use to account for investments in subsidiaries
and partnerships where it does not control but has significant influence?
- source_sentence: 'In December 2023, the FASB issued ASU No. 2023-09, ‘Income Taxes
(Topic 740): Improvements to Income Tax Disclosures.’ The ASU includes amendments
requiring enhanced income tax disclosures, primarily related to standardization
and disaggregation of rate reconciliation categories and income taxes paid by
jurisdiction.'
sentences:
- What are the total noncancelable purchase commitments as of December 31, 2023,
and how are they distributed over different time periods?
- What are the primary objectives of the Company's investment policy?
- What are the required amendments in the ASU No. 2023-09 regarding income tax disclosures?
- source_sentence: Income Taxes We are subject to income taxes in the U.S. and in
many foreign jurisdictions. Significant judgment is required in determining our
provision for income taxes, our deferred tax assets and liabilities and any valuation
allowance recorded against our net deferred tax assets that are not more likely
than not to be realized.
sentences:
- How does the company treat income taxes in its financial reports?
- How does YouTube contribute to users' experience according to the company's statement?
- When did The Charles Schwab Corporation change its corporate headquarters from
San Francisco to Westlake, Texas?
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: 0.7028571428571428
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8185714285714286
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.85
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8914285714285715
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7028571428571428
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2728571428571428
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16999999999999998
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08914285714285713
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7028571428571428
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8185714285714286
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.85
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8914285714285715
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.798341878406338
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7685107709750566
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7724628591268551
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.6985714285714286
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8157142857142857
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8528571428571429
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8985714285714286
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6985714285714286
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.27190476190476187
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17057142857142857
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08985714285714284
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6985714285714286
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8157142857142857
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8528571428571429
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8985714285714286
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7981564446782999
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7661643990929705
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7695965865934244
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.6971428571428572
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8085714285714286
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8428571428571429
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8885714285714286
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6971428571428572
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2695238095238095
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16857142857142857
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08885714285714284
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6971428571428572
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8085714285714286
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8428571428571429
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8885714285714286
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7917977544361884
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7611133786848071
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.765197446517495
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.6871428571428572
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8028571428571428
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8342857142857143
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8857142857142857
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6871428571428572
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2676190476190476
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16685714285714284
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08857142857142856
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6871428571428572
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8028571428571428
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8342857142857143
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8857142857142857
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7844783501102325
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7524892290249433
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.756590766205664
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.6571428571428571
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7814285714285715
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8114285714285714
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8585714285714285
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6571428571428571
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2604761904761905
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16228571428571428
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08585714285714285
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6571428571428571
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7814285714285715
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8114285714285714
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8585714285714285
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7570464835011314
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7245481859410431
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.729409564724743
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)
- **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("ethanteh/bge-base-financial-matryoshka")
# Run inference
sentences = [
'Income Taxes We are subject to income taxes in the U.S. and in many foreign jurisdictions. Significant judgment is required in determining our provision for income taxes, our deferred tax assets and liabilities and any valuation allowance recorded against our net deferred tax assets that are not more likely than not to be realized.',
'How does the company treat income taxes in its financial reports?',
"How does YouTube contribute to users' experience according to the company's statement?",
]
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]
```
## Evaluation
### Metrics
#### Information Retrieval
* Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64`
* Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | dim_768 | dim_512 | dim_256 | dim_128 | dim_64 |
|:--------------------|:-----------|:-----------|:-----------|:-----------|:----------|
| cosine_accuracy@1 | 0.7029 | 0.6986 | 0.6971 | 0.6871 | 0.6571 |
| cosine_accuracy@3 | 0.8186 | 0.8157 | 0.8086 | 0.8029 | 0.7814 |
| cosine_accuracy@5 | 0.85 | 0.8529 | 0.8429 | 0.8343 | 0.8114 |
| cosine_accuracy@10 | 0.8914 | 0.8986 | 0.8886 | 0.8857 | 0.8586 |
| cosine_precision@1 | 0.7029 | 0.6986 | 0.6971 | 0.6871 | 0.6571 |
| cosine_precision@3 | 0.2729 | 0.2719 | 0.2695 | 0.2676 | 0.2605 |
| cosine_precision@5 | 0.17 | 0.1706 | 0.1686 | 0.1669 | 0.1623 |
| cosine_precision@10 | 0.0891 | 0.0899 | 0.0889 | 0.0886 | 0.0859 |
| cosine_recall@1 | 0.7029 | 0.6986 | 0.6971 | 0.6871 | 0.6571 |
| cosine_recall@3 | 0.8186 | 0.8157 | 0.8086 | 0.8029 | 0.7814 |
| cosine_recall@5 | 0.85 | 0.8529 | 0.8429 | 0.8343 | 0.8114 |
| cosine_recall@10 | 0.8914 | 0.8986 | 0.8886 | 0.8857 | 0.8586 |
| **cosine_ndcg@10** | **0.7983** | **0.7982** | **0.7918** | **0.7845** | **0.757** |
| cosine_mrr@10 | 0.7685 | 0.7662 | 0.7611 | 0.7525 | 0.7245 |
| cosine_map@100 | 0.7725 | 0.7696 | 0.7652 | 0.7566 | 0.7294 |
## Training Details
### Training Dataset
#### json
* Dataset: json
* Size: 6,300 training samples
* Columns: positive and anchor
* Approximate statistics based on the first 1000 samples:
| | positive | anchor |
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 8 tokens
- mean: 46.74 tokens
- max: 512 tokens
| - min: 8 tokens
- mean: 20.52 tokens
- max: 39 tokens
|
* Samples:
| positive | anchor |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| During 2021, as a result of the enactment of a tax law and the closing of various acquisitions, the company concluded that it is no longer its intention to reinvest its undistributed earnings of its foreign TRSs indefinitely outside the United States. | What is the impact of a tax law change and acquisition closings on the company's intention regarding the reinvestment of undistributed earnings of its foreign TRSs? |
| In the year ended December 31, 2023, EBIT-adjusted decreased primarily due to: (1) increased Cost primarily due to increased campaigns and other warranty-related costs of $2.0 billion, increased EV-related charges of $1.9 billion primarily due to $1.6 billion in inventory adjustments to reflect the net realizable value at period end. | What factors contributed to the decrease in GM North America's EBIT-adjusted in 2023? |
| Peloton's e-commerce platform offers a range of products and services, including Peloton Bikes, Bike+, Tread, and Row products, along with one-on-one sales consultations. | What types of products and services does Peloton offer through its e-commerce platform? |
* Loss: [MatryoshkaLoss](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`: 4
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `fp16`: True
- `tf32`: False
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates
#### All Hyperparameters
Click to expand
- `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
- `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`: 4
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `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`: 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
- `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
- `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