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BGE base Financial Matryoshka

Name: bge-base-financial-matryoshka
Availability: InStock

This is a sentence-transformers model finetuned from 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
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
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

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:

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

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 with these parameters:
json
1{ 2 "loss": "MultipleNegativesRankingLoss", 3 "matryoshka_dims": [ 4 768, 5 512, 6 256, 7 128, 8 64 9 ], 10 "matryoshka_weights": [ 11 1, 12 1, 13 1, 14 1, 15 1 16 ], 17 "n_dims_per_step": -1 18}

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

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
0.2030	10	9.6119	-	-	-	-	-
0.4061	20	6.108	-	-	-	-	-
0.6091	30	3.9303	-	-	-	-	-
0.8122	40	3.4657	-	-	-	-	-
1.0	50	3.6929	0.7928	0.7891	0.7849	0.7732	0.7465
1.2030	60	1.86	-	-	-	-	-
1.4061	70	1.3879	-	-	-	-	-
1.6091	80	1.4367	-	-	-	-	-
1.8122	90	1.1032	-	-	-	-	-
2.0	100	1.696	0.7996	0.7966	0.7899	0.7815	0.7563
2.2030	110	1.0769	-	-	-	-	-
2.4061	120	0.6618	-	-	-	-	-
2.6091	130	0.912	-	-	-	-	-
2.8122	140	0.6271	-	-	-	-	-
3.0	150	0.9949	0.7984	0.7973	0.7925	0.7835	0.7574
3.2030	160	0.5734	-	-	-	-	-
3.4061	170	0.4934	-	-	-	-	-
3.6091	180	0.6593	-	-	-	-	-
3.8122	190	0.5452	-	-	-	-	-
3.934	196	-	0.7983	0.7982	0.7918	0.7845	0.757

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.11.11
Sentence Transformers: 3.3.1
Transformers: 4.48.1
PyTorch: 2.5.1+cu121
Accelerate: 1.2.1
Datasets: 2.19.1
Tokenizers: 0.21.0

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}
}

	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