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felipehsilveira/
bge-base-financial-matryoshka

Name: bge-base-financial-matryoshka
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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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("felipehsilveira/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'Our products compete with other commercially available products based primarily on efficacy, safety, tolerability, acceptance by doctors, ease of patient compliance, ease of use, price, insurance and other reimbursement coverage, distribution and marketing.',
    "What are the main factors influencing competition for the company's products?",
    'What was the impact of restructuring charges in 2022 on the company and what changes occurred in 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	0.6986
cosine_accuracy@3	0.83
cosine_accuracy@5	0.88
cosine_accuracy@10	0.9257
cosine_precision@1	0.6986
cosine_precision@3	0.2767
cosine_precision@5	0.176
cosine_precision@10	0.0926
cosine_recall@1	0.6986
cosine_recall@3	0.83
cosine_recall@5	0.88
cosine_recall@10	0.9257
cosine_ndcg@10	0.8142
cosine_mrr@10	0.7782
cosine_map@100	0.7808

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7014
cosine_accuracy@3	0.8329
cosine_accuracy@5	0.8857
cosine_accuracy@10	0.9229
cosine_precision@1	0.7014
cosine_precision@3	0.2776
cosine_precision@5	0.1771
cosine_precision@10	0.0923
cosine_recall@1	0.7014
cosine_recall@3	0.8329
cosine_recall@5	0.8857
cosine_recall@10	0.9229
cosine_ndcg@10	0.8134
cosine_mrr@10	0.7781
cosine_map@100	0.7809

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7
cosine_accuracy@3	0.84
cosine_accuracy@5	0.8714
cosine_accuracy@10	0.9086
cosine_precision@1	0.7
cosine_precision@3	0.28
cosine_precision@5	0.1743
cosine_precision@10	0.0909
cosine_recall@1	0.7
cosine_recall@3	0.84
cosine_recall@5	0.8714
cosine_recall@10	0.9086
cosine_ndcg@10	0.8077
cosine_mrr@10	0.775
cosine_map@100	0.7785

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6943
cosine_accuracy@3	0.82
cosine_accuracy@5	0.8557
cosine_accuracy@10	0.9029
cosine_precision@1	0.6943
cosine_precision@3	0.2733
cosine_precision@5	0.1711
cosine_precision@10	0.0903
cosine_recall@1	0.6943
cosine_recall@3	0.82
cosine_recall@5	0.8557
cosine_recall@10	0.9029
cosine_ndcg@10	0.7991
cosine_mrr@10	0.7659
cosine_map@100	0.7697

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6614
cosine_accuracy@3	0.7843
cosine_accuracy@5	0.8271
cosine_accuracy@10	0.8886
cosine_precision@1	0.6614
cosine_precision@3	0.2614
cosine_precision@5	0.1654
cosine_precision@10	0.0889
cosine_recall@1	0.6614
cosine_recall@3	0.7843
cosine_recall@5	0.8271
cosine_recall@10	0.8886
cosine_ndcg@10	0.7731
cosine_mrr@10	0.7366
cosine_map@100	0.7404

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: 45.44 tokens
max: 301 tokens
min: 7 tokens
mean: 20.3 tokens
max: 51 tokens

Samples:

positive	anchor
`The Centers for Medicare & Medicaid Services issued a final rule in October 2023 for the calendar year 2024, estimating a productivity-adjusted market basket increase of 2.1% in average reimbursement to ESRD facilities.`	`What is the projected impact on average reimbursement to ESRD facilities in 2024 due to the final rule issued by CMS?`
`Company Adjusted EBIT Margin is derived by dividing the Company adjusted EBIT by Company revenue, which is a non-GAAP measure useful for evaluating the company's operating results.`	`How is the Company Adjusted EBIT Margin calculated?`
`The provision for credit losses was $4 million for the year ended December 31, 202 serviLists of account holders responsible for and the state of the economy, our credit standards, our risk assessments, and the judgment of our employees responsible for granting credit.`	`What factors influence the provision for credit losses at Las Vegas Sands Corp.?`

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
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
torch_empty_cache_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
eval_on_start: False
eval_use_gather_object: 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	1.5176	-	-	-	-	-
0.9746	12	-	0.7500	0.7642	0.7680	0.7079	0.7708
1.6244	20	0.6868	-	-	-	-	-
1.9492	24	-	0.7657	0.7746	0.7784	0.7323	0.7816
2.4365	30	0.4738	-	-	-	-	-
2.9239	36	-	0.7691	0.7780	0.7790	0.7402	0.7796
3.2487	40	0.3934	-	-	-	-	-
3.8985	48	-	0.7697	0.7785	0.7809	0.7404	0.7808

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.11.9
Sentence Transformers: 3.0.1
Transformers: 4.44.2
PyTorch: 2.4.0+cu121
Accelerate: 0.33.0
Datasets: 2.21.0
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: 6 tokens mean: 45.44 tokens max: 301 tokens	min: 7 tokens mean: 20.3 tokens max: 51 tokens