You can deploy this model and start earning money today!

Views

No views yet

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("IlhamEbdesk/bge-base-financial-matryoshka")
# Run inference
sentences = [
    "During September 2023, the Company entered into a third amended and restated revolving credit agreement with Bank of America, N.A., as administrative agent, swing line lender and a letter of credit issuer and lender and certain other financial institutions, as lenders thereto (the 'Amended Revolving Credit Agreement'), which provides the Company with commitments having a maximum aggregate principal amount of $1.25 billion, effective as of September 5, 2023. The Amended Revolving Credit Agreement also provides for a potential additional incremental commitment increase of up to $500.0 million subject to agreement of the lenders. The Amended Revolving Credit Agreement contains certain financial covenants setting forth leverage and coverage requirements, and certain other limitations typical of an investment grade facility, including with respect to liens, mergers and incurrence of indebtedness. The Amended Revolving Credit Agreement extends through September 5, 2028.",
    'What is the function of the amended revolving credit agreement that the Company entered into with Bank of America in September 2023?',
    'What position does Brad D. Smith currently hold?',
]
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	0.6617
cosine_accuracy@3	0.7933
cosine_accuracy@5	0.8365
cosine_accuracy@10	0.8851
cosine_precision@1	0.6617
cosine_precision@3	0.2644
cosine_precision@5	0.1673
cosine_precision@10	0.0885
cosine_recall@1	0.6617
cosine_recall@3	0.7933
cosine_recall@5	0.8365
cosine_recall@10	0.8851
cosine_ndcg@10	0.7731
cosine_mrr@10	0.7373
cosine_map@100	0.7413

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.661
cosine_accuracy@3	0.7881
cosine_accuracy@5	0.8352
cosine_accuracy@10	0.8835
cosine_precision@1	0.661
cosine_precision@3	0.2627
cosine_precision@5	0.167
cosine_precision@10	0.0883
cosine_recall@1	0.661
cosine_recall@3	0.7881
cosine_recall@5	0.8352
cosine_recall@10	0.8835
cosine_ndcg@10	0.7713
cosine_mrr@10	0.7355
cosine_map@100	0.7397

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6508
cosine_accuracy@3	0.7795
cosine_accuracy@5	0.824
cosine_accuracy@10	0.874
cosine_precision@1	0.6508
cosine_precision@3	0.2598
cosine_precision@5	0.1648
cosine_precision@10	0.0874
cosine_recall@1	0.6508
cosine_recall@3	0.7795
cosine_recall@5	0.824
cosine_recall@10	0.874
cosine_ndcg@10	0.7614
cosine_mrr@10	0.7255
cosine_map@100	0.7298

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6217
cosine_accuracy@3	0.7541
cosine_accuracy@5	0.7987
cosine_accuracy@10	0.8546
cosine_precision@1	0.6217
cosine_precision@3	0.2514
cosine_precision@5	0.1597
cosine_precision@10	0.0855
cosine_recall@1	0.6217
cosine_recall@3	0.7541
cosine_recall@5	0.7987
cosine_recall@10	0.8546
cosine_ndcg@10	0.7369
cosine_mrr@10	0.6994
cosine_map@100	0.7043

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.5648
cosine_accuracy@3	0.7027
cosine_accuracy@5	0.7478
cosine_accuracy@10	0.8013
cosine_precision@1	0.5648
cosine_precision@3	0.2342
cosine_precision@5	0.1496
cosine_precision@10	0.0801
cosine_recall@1	0.5648
cosine_recall@3	0.7027
cosine_recall@5	0.7478
cosine_recall@10	0.8013
cosine_ndcg@10	0.6818
cosine_mrr@10	0.6437
cosine_map@100	0.6495

Training Details

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
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: False
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	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
0.7273	1	0.6707	0.7045	0.7171	0.6067	0.7188
1.4545	2	0.6912	0.7205	0.7302	0.6313	0.7327
2.9091	4	0.7043	0.7298	0.7397	0.6495	0.7413

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.0.1
Transformers: 4.41.2
PyTorch: 2.1.2+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}
}