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("NickyNicky/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'Our Records Management and Data Management service revenue growth is being negatively impacted by declining activity rates as stored records and tapes are becoming less active and more archival.',
    'How is Iron Mountain addressing the decline in activity rates in their Records and Data Management services?',
    'What services do companies that build fiber-based networks provide in the Connectivity & Platforms markets?',
]
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	0.7057
cosine_accuracy@3	0.8457
cosine_accuracy@5	0.8786
cosine_accuracy@10	0.9114
cosine_precision@1	0.7057
cosine_precision@3	0.2819
cosine_precision@5	0.1757
cosine_precision@10	0.0911
cosine_recall@1	0.7057
cosine_recall@3	0.8457
cosine_recall@5	0.8786
cosine_recall@10	0.9114
cosine_ndcg@10	0.8125
cosine_mrr@10	0.7804
cosine_map@100	0.7839

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7071
cosine_accuracy@3	0.8429
cosine_accuracy@5	0.8743
cosine_accuracy@10	0.9114
cosine_precision@1	0.7071
cosine_precision@3	0.281
cosine_precision@5	0.1749
cosine_precision@10	0.0911
cosine_recall@1	0.7071
cosine_recall@3	0.8429
cosine_recall@5	0.8743
cosine_recall@10	0.9114
cosine_ndcg@10	0.8127
cosine_mrr@10	0.7807
cosine_map@100	0.7841

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7029
cosine_accuracy@3	0.8357
cosine_accuracy@5	0.8686
cosine_accuracy@10	0.9071
cosine_precision@1	0.7029
cosine_precision@3	0.2786
cosine_precision@5	0.1737
cosine_precision@10	0.0907
cosine_recall@1	0.7029
cosine_recall@3	0.8357
cosine_recall@5	0.8686
cosine_recall@10	0.9071
cosine_ndcg@10	0.8087
cosine_mrr@10	0.7769
cosine_map@100	0.7806

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6914
cosine_accuracy@3	0.82
cosine_accuracy@5	0.8557
cosine_accuracy@10	0.9014
cosine_precision@1	0.6914
cosine_precision@3	0.2733
cosine_precision@5	0.1711
cosine_precision@10	0.0901
cosine_recall@1	0.6914
cosine_recall@3	0.82
cosine_recall@5	0.8557
cosine_recall@10	0.9014
cosine_ndcg@10	0.7981
cosine_mrr@10	0.765
cosine_map@100	0.7689

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6543
cosine_accuracy@3	0.7886
cosine_accuracy@5	0.8329
cosine_accuracy@10	0.8829
cosine_precision@1	0.6543
cosine_precision@3	0.2629
cosine_precision@5	0.1666
cosine_precision@10	0.0883
cosine_recall@1	0.6543
cosine_recall@3	0.7886
cosine_recall@5	0.8329
cosine_recall@10	0.8829
cosine_ndcg@10	0.769
cosine_mrr@10	0.7325
cosine_map@100	0.7369

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: 10 tokens
mean: 46.55 tokens
max: 512 tokens

min: 7 tokens
mean: 20.56 tokens
max: 42 tokens

	positive	anchor
type	string	string
details	min: 10 tokens mean: 46.55 tokens max: 512 tokens	min: 7 tokens mean: 20.56 tokens max: 42 tokens

Samples:

positive	anchor
`Internationally, Visa Inc.'s commercial payments volume grew by 23% from $407 billion in 2021 to $500 billion in 2022.`	`What was the growth rate of Visa Inc.'s commercial payments volume internationally between 2021 and 2022?`
`The consolidated financial statements and accompanying notes listed in Part IV, Item 15(a)(1) of this Annual Report on Form 10-K are included immediately following Part IV hereof.`	`Where can one find the consolidated financial statements and accompanying notes in the Annual Report on Form 10-K?`
`The additional paid-in capital at the end of 2023 was recorded as $114,519 million.`	`What was the amount recorded for additional paid-in capital at the end of 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: 80
per_device_eval_batch_size: 16
gradient_accumulation_steps: 16
learning_rate: 2e-05
num_train_epochs: 15
lr_scheduler_type: cosine
warmup_ratio: 0.1
bf16: True
tf32: 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: 80
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: 15
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: False
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
0.8101	4	-	0.7066	0.7309	0.7390	0.6462	0.7441
1.8228	9	-	0.7394	0.7497	0.7630	0.6922	0.7650
2.0253	10	2.768	-	-	-	-	-
2.8354	14	-	0.7502	0.7625	0.7767	0.7208	0.7787
3.8481	19	-	0.7553	0.7714	0.7804	0.7234	0.7802
4.0506	20	1.1294	-	-	-	-	-
4.8608	24	-	0.7577	0.7769	0.7831	0.7327	0.7858
5.8734	29	-	0.7616	0.7775	0.7832	0.7335	0.7876
6.0759	30	0.7536	-	-	-	-	-
6.8861	34	-	0.7624	0.7788	0.7832	0.7352	0.7882
7.8987	39	-	0.7665	0.7795	0.7814	0.7359	0.7861
8.1013	40	0.5846	-	-	-	-	-
8.9114	44	-	0.7688	0.7801	0.7828	0.7360	0.7857
9.9241	49	-	0.7698	0.7804	0.7836	0.7367	0.7840
10.1266	50	0.5187	-	-	-	-	-
10.9367	54	-	0.7692	0.7801	0.7827	0.7383	0.7837
11.9494	59	-	0.7698	0.7801	0.7834	0.7377	0.7849
12.1519	60	0.4949	0.7689	0.7806	0.7841	0.7369	0.7839

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.0.1
Transformers: 4.41.2
PyTorch: 2.2.0+cu121
Accelerate: 0.31.0
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}
}

NickyNicky
/

bge-base-financial-matryoshka_test_4

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 NickyNicky/bge-base-financial-matryoshka_test_4

Evaluation results