BGE base Financial Matryoshka

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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("uonyeka/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'The return on equity for 2023 was 27.0%.',
    'What was the return on equity for 2023?',
    'What was the total net property and equipment as of December 31, 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	0.7029
cosine_accuracy@3	0.84
cosine_accuracy@5	0.8829
cosine_accuracy@10	0.9214
cosine_precision@1	0.7029
cosine_precision@3	0.28
cosine_precision@5	0.1766
cosine_precision@10	0.0921
cosine_recall@1	0.7029
cosine_recall@3	0.84
cosine_recall@5	0.8829
cosine_recall@10	0.9214
cosine_ndcg@10	0.8149
cosine_mrr@10	0.7804
cosine_map@100	0.7839

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7029
cosine_accuracy@3	0.8386
cosine_accuracy@5	0.8871
cosine_accuracy@10	0.9257
cosine_precision@1	0.7029
cosine_precision@3	0.2795
cosine_precision@5	0.1774
cosine_precision@10	0.0926
cosine_recall@1	0.7029
cosine_recall@3	0.8386
cosine_recall@5	0.8871
cosine_recall@10	0.9257
cosine_ndcg@10	0.8153
cosine_mrr@10	0.7797
cosine_map@100	0.783

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7057
cosine_accuracy@3	0.8329
cosine_accuracy@5	0.8729
cosine_accuracy@10	0.9114
cosine_precision@1	0.7057
cosine_precision@3	0.2776
cosine_precision@5	0.1746
cosine_precision@10	0.0911
cosine_recall@1	0.7057
cosine_recall@3	0.8329
cosine_recall@5	0.8729
cosine_recall@10	0.9114
cosine_ndcg@10	0.8097
cosine_mrr@10	0.777
cosine_map@100	0.7811

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.68
cosine_accuracy@3	0.8286
cosine_accuracy@5	0.8671
cosine_accuracy@10	0.91
cosine_precision@1	0.68
cosine_precision@3	0.2762
cosine_precision@5	0.1734
cosine_precision@10	0.091
cosine_recall@1	0.68
cosine_recall@3	0.8286
cosine_recall@5	0.8671
cosine_recall@10	0.91
cosine_ndcg@10	0.7972
cosine_mrr@10	0.7608
cosine_map@100	0.7644

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6614
cosine_accuracy@3	0.7943
cosine_accuracy@5	0.83
cosine_accuracy@10	0.8757
cosine_precision@1	0.6614
cosine_precision@3	0.2648
cosine_precision@5	0.166
cosine_precision@10	0.0876
cosine_recall@1	0.6614
cosine_recall@3	0.7943
cosine_recall@5	0.83
cosine_recall@10	0.8757
cosine_ndcg@10	0.7702
cosine_mrr@10	0.7363
cosine_map@100	0.7414

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: 6 tokens
mean: 45.2 tokens
max: 439 tokens

min: 7 tokens
mean: 20.41 tokens
max: 43 tokens

	positive	anchor
type	string	string
details	min: 6 tokens mean: 45.2 tokens max: 439 tokens	min: 7 tokens mean: 20.41 tokens max: 43 tokens

Samples:

positive	anchor
`The cash equities rate per contract (per 100 shares) for NYSE increased by 6%, from $0.045 in 2022 to $0.048 in 2023.`	`What was the change in the rate per contract for NYSE cash equities from 2022 to 2023?`
`Item 3 specifies that the information regarding Legal Proceedings is sourced from Note 19 of the Notes to Consolidated Financial Statements included in Item 8.`	`What is the content source for the information requested by Item 3 concerning Legal Proceedings?`
`North America's operating income for the fiscal year ended October 1, 2023, was $5,495.7 million, up from $4,486.5 million in fiscal 2022.`	`What was the increase in North America's operating income from fiscal 2022 to fiscal 2023?`

Loss: MatryoshkaLoss with these parameters:

{
    "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
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
tf32: 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: True
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	1.575	-	-	-	-	-
0.9746	12	-	0.7437	0.7623	0.7682	0.7114	0.7652
1.6244	20	0.697	-	-	-	-	-
1.9492	24	-	0.7619	0.7760	0.7824	0.7346	0.7826
2.4365	30	0.4724	-	-	-	-	-
2.9239	36	-	0.7639	0.7808	0.7831	0.7398	0.7834
3.2487	40	0.3999	-	-	-	-	-
3.8985	48	-	0.7644	0.7811	0.783	0.7414	0.7839

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.0.0
Transformers: 4.41.2
PyTorch: 2.1.2+cu121
Accelerate: 0.30.1
Datasets: 2.19.1
Tokenizers: 0.19.1

Citation

BibTeX

Sentence Transformers

@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

@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

@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}
}

uonyeka
/

bge-base-financial-matryoshka

BGE base Financial Matryoshka

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Evaluation

Metrics

Information Retrieval

Information Retrieval

Information Retrieval

Information Retrieval

Information Retrieval

Training Details

Training Dataset

Unnamed Dataset

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

MatryoshkaLoss

MultipleNegativesRankingLoss

Model tree for uonyeka/bge-base-financial-matryoshka

Evaluation results