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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("akashmaggon/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'The table presents our market risk by asset category for positions accounted for at fair value or accounted for at the lower of cost or fair value, that are not included in VaR. As of December 2023, equity was at $1,562 million and debt was at $2,446 million.',
    "What are the market risk values for Goldman Sachs' equity and debt positions not included in VaR as of December 2023?",
    "What was the conclusion of the Company's review regarding the impact of the American Rescue Plan, the Consolidated Appropriations Act, 2021, and related tax provisions on its business for the fiscal year ended 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	0.6957
cosine_accuracy@3	0.8371
cosine_accuracy@5	0.8714
cosine_accuracy@10	0.9243
cosine_precision@1	0.6957
cosine_precision@3	0.279
cosine_precision@5	0.1743
cosine_precision@10	0.0924
cosine_recall@1	0.6957
cosine_recall@3	0.8371
cosine_recall@5	0.8714
cosine_recall@10	0.9243
cosine_ndcg@10	0.8105
cosine_mrr@10	0.7742
cosine_map@100	0.7773

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7
cosine_accuracy@3	0.8286
cosine_accuracy@5	0.8671
cosine_accuracy@10	0.9186
cosine_precision@1	0.7
cosine_precision@3	0.2762
cosine_precision@5	0.1734
cosine_precision@10	0.0919
cosine_recall@1	0.7
cosine_recall@3	0.8286
cosine_recall@5	0.8671
cosine_recall@10	0.9186
cosine_ndcg@10	0.809
cosine_mrr@10	0.774
cosine_map@100	0.7776

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6929
cosine_accuracy@3	0.8186
cosine_accuracy@5	0.8586
cosine_accuracy@10	0.91
cosine_precision@1	0.6929
cosine_precision@3	0.2729
cosine_precision@5	0.1717
cosine_precision@10	0.091
cosine_recall@1	0.6929
cosine_recall@3	0.8186
cosine_recall@5	0.8586
cosine_recall@10	0.91
cosine_ndcg@10	0.8017
cosine_mrr@10	0.767
cosine_map@100	0.7712

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6871
cosine_accuracy@3	0.8071
cosine_accuracy@5	0.8586
cosine_accuracy@10	0.8986
cosine_precision@1	0.6871
cosine_precision@3	0.269
cosine_precision@5	0.1717
cosine_precision@10	0.0899
cosine_recall@1	0.6871
cosine_recall@3	0.8071
cosine_recall@5	0.8586
cosine_recall@10	0.8986
cosine_ndcg@10	0.7921
cosine_mrr@10	0.7581
cosine_map@100	0.7627

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6643
cosine_accuracy@3	0.7843
cosine_accuracy@5	0.8257
cosine_accuracy@10	0.8729
cosine_precision@1	0.6643
cosine_precision@3	0.2614
cosine_precision@5	0.1651
cosine_precision@10	0.0873
cosine_recall@1	0.6643
cosine_recall@3	0.7843
cosine_recall@5	0.8257
cosine_recall@10	0.8729
cosine_ndcg@10	0.769
cosine_mrr@10	0.7358
cosine_map@100	0.7407

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: 7 tokens
mean: 44.39 tokens
max: 512 tokens
min: 7 tokens
mean: 20.64 tokens
max: 51 tokens

Samples:

positive	anchor
`Johnson & Johnson reported cash and cash equivalents of $21,859 million as of the end of 2023.`	`What was the amount of cash and cash equivalents reported by Johnson & Johnson at the end of 2023?`
`Johnson & Johnson's consolidated statements of earnings for 2023 reported total net earnings of $35,153 million.`	`What was the total net earnings for Johnson & Johnson in 2023?`
`As of December 31, 2023, short-term investments were valued at $236,118 thousand and long-term investments at $86,676 thousand.`	`What is the total value of short-term and long-term investments held by the company as of December 31, 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
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: 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: 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
0.8122	10	1.5779	-	-	-	-	-
0.9746	12	-	0.7388	0.7509	0.7604	0.7081	0.7579
1.6244	20	0.6572	-	-	-	-	-
1.9492	24	-	0.7612	0.7670	0.7729	0.7269	0.7705
2.4365	30	0.4661	-	-	-	-	-
2.9239	36	-	0.7623	0.7702	0.7771	0.7386	0.7758
3.2487	40	0.3774	-	-	-	-	-
3.8985	48	-	0.7627	0.7712	0.7776	0.7407	0.7773

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.0.1
Transformers: 4.41.2
PyTorch: 2.3.1+cu121
Accelerate: 0.32.1
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: 7 tokens mean: 44.39 tokens max: 512 tokens	min: 7 tokens mean: 20.64 tokens max: 51 tokens