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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. 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 tokens
Similarity Function: Cosine Similarity

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("Naruke/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'As part of our solar energy system and energy storage contracts, we may provide the customer with performance guarantees that commit that the underlying system will meet or exceed the minimum energy generation or performance requirements specified in the contract.',
    'What types of guarantees does Tesla provide to its solar and energy storage customers?',
    'How many full-time employees did Microsoft report as of June 30, 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]

Evaluation

Metrics

Information Retrieval

Dataset: dim_768
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.71
cosine_accuracy@3	0.84
cosine_accuracy@5	0.8686
cosine_accuracy@10	0.9143
cosine_precision@1	0.71
cosine_precision@3	0.28
cosine_precision@5	0.1737
cosine_precision@10	0.0914
cosine_recall@1	0.71
cosine_recall@3	0.84
cosine_recall@5	0.8686
cosine_recall@10	0.9143
cosine_ndcg@10	0.8125
cosine_mrr@10	0.7798
cosine_map@100	0.7826

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7043
cosine_accuracy@3	0.8357
cosine_accuracy@5	0.8657
cosine_accuracy@10	0.9114
cosine_precision@1	0.7043
cosine_precision@3	0.2786
cosine_precision@5	0.1731
cosine_precision@10	0.0911
cosine_recall@1	0.7043
cosine_recall@3	0.8357
cosine_recall@5	0.8657
cosine_recall@10	0.9114
cosine_ndcg@10	0.8078
cosine_mrr@10	0.7745
cosine_map@100	0.7776

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7029
cosine_accuracy@3	0.8229
cosine_accuracy@5	0.8586
cosine_accuracy@10	0.8971
cosine_precision@1	0.7029
cosine_precision@3	0.2743
cosine_precision@5	0.1717
cosine_precision@10	0.0897
cosine_recall@1	0.7029
cosine_recall@3	0.8229
cosine_recall@5	0.8586
cosine_recall@10	0.8971
cosine_ndcg@10	0.8004
cosine_mrr@10	0.7693
cosine_map@100	0.7733

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6771
cosine_accuracy@3	0.8143
cosine_accuracy@5	0.8543
cosine_accuracy@10	0.8971
cosine_precision@1	0.6771
cosine_precision@3	0.2714
cosine_precision@5	0.1709
cosine_precision@10	0.0897
cosine_recall@1	0.6771
cosine_recall@3	0.8143
cosine_recall@5	0.8543
cosine_recall@10	0.8971
cosine_ndcg@10	0.7887
cosine_mrr@10	0.7538
cosine_map@100	0.7573

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6643
cosine_accuracy@3	0.7814
cosine_accuracy@5	0.8129
cosine_accuracy@10	0.86
cosine_precision@1	0.6643
cosine_precision@3	0.2605
cosine_precision@5	0.1626
cosine_precision@10	0.086
cosine_recall@1	0.6643
cosine_recall@3	0.7814
cosine_recall@5	0.8129
cosine_recall@10	0.86
cosine_ndcg@10	0.76
cosine_mrr@10	0.7283
cosine_map@100	0.7331

Training Details

Training Dataset

Unnamed Dataset

Size: 6,300 training samples
Columns: positive and anchor
Approximate statistics based on the first 1000 samples:
positive anchor
type string string
details
min: 9 tokens
mean: 45.57 tokens
max: 289 tokens
min: 9 tokens
mean: 20.32 tokens
max: 51 tokens

Samples:

positive	anchor
`The detailed information about commitments and contingencies related to legal proceedings is included under Note 13 in Part II, Item 8 of the Annual Report.`	`Where can detailed information about the commitments and contingencies related to legal proceedings be found in the Annual Report on Form 10-K?`
`American Express's decision to reinvest gains into its business will depend on regulatory and other approvals, consultation requirements, the execution of ancillary agreements, the cost and availability of financing for the purchaser to fund the transaction and the potential loss of key customers, vendors and other business partners and management’s decisions regarding future operations, strategies and business initiatives.`	`What factors influence American Express's decision to reinvest gains into its business?`
`Lease obligations as of June 30, 2023, related to office space and various facilities totaled $883.1 million, with lease terms ranging from one to 21 years and are mostly renewable.`	`How much were lease obligations related to office space and other facilities as of June 30, 2023, and what were the terms?`

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
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
gradient_accumulation_steps: 16
learning_rate: 2e-05
num_train_epochs: 2
lr_scheduler_type: cosine
warmup_ratio: 0.1
bf16: True
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: 16
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: 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: True
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: None
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

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
0.4061	10	0.9835	-	-	-	-	-
0.8122	20	0.4319	-	-	-	-	-
0.9746	24	-	0.7541	0.7729	0.7738	0.7242	0.7786
1.2183	30	0.3599	-	-	-	-	-
1.6244	40	0.2596	-	-	-	-	-
1.9492	48	-	0.7573	0.7733	0.7776	0.7331	0.7826

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.0.1
Transformers: 4.41.2
PyTorch: 2.3.0+cu121
Accelerate: 0.32.1
Datasets: 2.20.0
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}
}

	positive	anchor
type	string	string
details	min: 9 tokens mean: 45.57 tokens max: 289 tokens	min: 9 tokens mean: 20.32 tokens max: 51 tokens