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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 tokens
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("Yuki20/bge-base-financial-matryoshka")
# Run inference
sentences = [
    "As of December 31, 2023, the fair value and amortized cost, net of valuation allowance, for the Republic of Korea's government securities were $1,784 million and $1,723 million respectively.",
    "What was the fair value and amortized cost, net of valuation allowance, for the Republic of Korea's government securities as of December 31, 2023?",
    'How does the company advance autonomous vehicle technology?',
]
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	0.6871
cosine_accuracy@3	0.8286
cosine_accuracy@5	0.8571
cosine_accuracy@10	0.9071
cosine_precision@1	0.6871
cosine_precision@3	0.2762
cosine_precision@5	0.1714
cosine_precision@10	0.0907
cosine_recall@1	0.6871
cosine_recall@3	0.8286
cosine_recall@5	0.8571
cosine_recall@10	0.9071
cosine_ndcg@10	0.7982
cosine_mrr@10	0.7633
cosine_map@100	0.767

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.69
cosine_accuracy@3	0.8171
cosine_accuracy@5	0.8543
cosine_accuracy@10	0.9043
cosine_precision@1	0.69
cosine_precision@3	0.2724
cosine_precision@5	0.1709
cosine_precision@10	0.0904
cosine_recall@1	0.69
cosine_recall@3	0.8171
cosine_recall@5	0.8543
cosine_recall@10	0.9043
cosine_ndcg@10	0.7977
cosine_mrr@10	0.7636
cosine_map@100	0.7675

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6857
cosine_accuracy@3	0.8143
cosine_accuracy@5	0.8514
cosine_accuracy@10	0.8957
cosine_precision@1	0.6857
cosine_precision@3	0.2714
cosine_precision@5	0.1703
cosine_precision@10	0.0896
cosine_recall@1	0.6857
cosine_recall@3	0.8143
cosine_recall@5	0.8514
cosine_recall@10	0.8957
cosine_ndcg@10	0.7916
cosine_mrr@10	0.7582
cosine_map@100	0.7624

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6757
cosine_accuracy@3	0.8
cosine_accuracy@5	0.8414
cosine_accuracy@10	0.8886
cosine_precision@1	0.6757
cosine_precision@3	0.2667
cosine_precision@5	0.1683
cosine_precision@10	0.0889
cosine_recall@1	0.6757
cosine_recall@3	0.8
cosine_recall@5	0.8414
cosine_recall@10	0.8886
cosine_ndcg@10	0.782
cosine_mrr@10	0.7478
cosine_map@100	0.7524

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6414
cosine_accuracy@3	0.7657
cosine_accuracy@5	0.7957
cosine_accuracy@10	0.8586
cosine_precision@1	0.6414
cosine_precision@3	0.2552
cosine_precision@5	0.1591
cosine_precision@10	0.0859
cosine_recall@1	0.6414
cosine_recall@3	0.7657
cosine_recall@5	0.7957
cosine_recall@10	0.8586
cosine_ndcg@10	0.748
cosine_mrr@10	0.7129
cosine_map@100	0.7185

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: 6 tokens
mean: 45.58 tokens
max: 289 tokens
min: 9 tokens
mean: 20.34 tokens
max: 41 tokens

Samples:

positive	anchor
`Billed business grew significantly over the past two years, increasing from $228.2 billion in 2021 to $281.6 billion in 2022, and reaching $329.5 billion in 2023.`	`How did billed business figures change from 2021 to 2023 as stated in the text?`
`The Federal Reserve may limit an FHC’s ability to conduct permissible activities if it or any of its depository institution subsidiaries fails to maintain a well-capitalized and well-managed status. If non-compliant after 180 days, the Federal Reserve may require the FHC to divest its depository institution subsidiaries or cease all FHC Activities.`	`What happens if an FHC does not meet the Federal Reserve's eligibility requirements?`
`For the fiscal year ending January 28, 2023, the basic net income per share was calculated to be $7.24, based on the net income and weighted average number of shares outstanding.`	`What was the basic net income per share in the fiscal year ending January 28, 2023?`

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: 32
per_device_eval_batch_size: 16
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: 32
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: 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: 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_768_cosine_map@100	dim_512_cosine_map@100	dim_256_cosine_map@100	dim_128_cosine_map@100	dim_64_cosine_map@100
0.8122	10	1.588	-	-	-	-	-
0.9746	12	-	0.7593	0.7550	0.7472	0.7347	0.6970
1.6244	20	0.7059	-	-	-	-	-
1.9492	24	-	0.7623	0.7652	0.7559	0.7517	0.7127
2.4365	30	0.4826	-	-	-	-	-
2.9239	36	-	0.7675	0.7683	0.7603	0.7512	0.7166
3.2487	40	0.3992	-	-	-	-	-
3.8985	48	-	0.767	0.7675	0.7624	0.7524	0.7185

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.2.0
Transformers: 4.41.2
PyTorch: 2.1.2+cu121
Accelerate: 0.34.2
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}
}

	positive	anchor
type	string	string
details	min: 6 tokens mean: 45.58 tokens max: 289 tokens	min: 9 tokens mean: 20.34 tokens max: 41 tokens