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anishareddyalla/
bge-base-financial-matryoshka-anisha

Name: bge-base-financial-matryoshka-anisha
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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("anishareddyalla/bge-base-financial-matryoshka-anisha")
# Run inference
sentences = [
    "In 1983, Walmart opened its first Sam's Club, and in 1988, it opened its first supercenter.",
    "When did Walmart open its first Sam's Club and supercenter?",
    'Which standards and guidelines does the company use for informing its sustainability disclosures?',
]
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	0.8371
cosine_accuracy@5	0.8729
cosine_accuracy@10	0.9186
cosine_precision@1	0.7029
cosine_precision@3	0.279
cosine_precision@5	0.1746
cosine_precision@10	0.0919
cosine_recall@1	0.7029
cosine_recall@3	0.8371
cosine_recall@5	0.8729
cosine_recall@10	0.9186
cosine_ndcg@10	0.812
cosine_mrr@10	0.7777
cosine_map@100	0.781

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6986
cosine_accuracy@3	0.8329
cosine_accuracy@5	0.8643
cosine_accuracy@10	0.9243
cosine_precision@1	0.6986
cosine_precision@3	0.2776
cosine_precision@5	0.1729
cosine_precision@10	0.0924
cosine_recall@1	0.6986
cosine_recall@3	0.8329
cosine_recall@5	0.8643
cosine_recall@10	0.9243
cosine_ndcg@10	0.8105
cosine_mrr@10	0.7743
cosine_map@100	0.7771

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6943
cosine_accuracy@3	0.8271
cosine_accuracy@5	0.8586
cosine_accuracy@10	0.9086
cosine_precision@1	0.6943
cosine_precision@3	0.2757
cosine_precision@5	0.1717
cosine_precision@10	0.0909
cosine_recall@1	0.6943
cosine_recall@3	0.8271
cosine_recall@5	0.8586
cosine_recall@10	0.9086
cosine_ndcg@10	0.8026
cosine_mrr@10	0.7687
cosine_map@100	0.7726

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6886
cosine_accuracy@3	0.8157
cosine_accuracy@5	0.8571
cosine_accuracy@10	0.9071
cosine_precision@1	0.6886
cosine_precision@3	0.2719
cosine_precision@5	0.1714
cosine_precision@10	0.0907
cosine_recall@1	0.6886
cosine_recall@3	0.8157
cosine_recall@5	0.8571
cosine_recall@10	0.9071
cosine_ndcg@10	0.7973
cosine_mrr@10	0.7622
cosine_map@100	0.7657

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.66
cosine_accuracy@3	0.7986
cosine_accuracy@5	0.8357
cosine_accuracy@10	0.8829
cosine_precision@1	0.66
cosine_precision@3	0.2662
cosine_precision@5	0.1671
cosine_precision@10	0.0883
cosine_recall@1	0.66
cosine_recall@3	0.7986
cosine_recall@5	0.8357
cosine_recall@10	0.8829
cosine_ndcg@10	0.7716
cosine_mrr@10	0.7361
cosine_map@100	0.7401

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: 8 tokens
mean: 46.43 tokens
max: 439 tokens
min: 8 tokens
mean: 20.76 tokens
max: 43 tokens

Samples:

positive	anchor
`The Company’s human capital management strategy is built on three fundamental focus areas: Attracting and recruiting the best talent, Developing and retaining talent, Empowering and inspiring talent.`	`What strategies are outlined in the Company's human capital management?`
Opinion on the Consolidated Financial Statements We have audited the accompanying consolidated balance sheets of Costco Wholesale Corporation and subsidiaries (the Company) as of September 3, 2023, and August 28, 2022, the related consolidated statements of income, comprehensive income, equity, and cash flows for the 53-week period ended September 3, 2023, and the 52-week periods ended August 28, 2022, and August 29, 2021, and the related notes (collectively, the consolidated financial statements). In our opinion, the consolidated financial statements present fairly, in all material respects, the financial position of the Company as of September 3, 2023, and August 28, 2022, and the results of its operations and its cash flows for each of the 53-week period ended September 3, 2023, and the 52-week periods ended August 28, 2022, and August 29, 2021, in conformity with U.S. generally accepted accounting principles.	`What was the opinion of the independent registered public accounting firm on Costco Wholesale Corporation's consolidated financial statements for the year ended September 3, 2023?`
`Nonperforming loans and leases are generally those that have been placed on nonaccrual status, such as when they are 90 days past due or have confirmed cases of fraud or bankruptcy. Additionally, specific types of loans like consumer real estate-secured loans are classified as nonperforming at 90 days past due unless they are fully insured, and commercial loans and leases are classified as nonperforming when past due 90 days or more unless well-secured and in the process of collection.`	`What criteria are used to classify loans and leases as nonperforming according to the described credit policy?`

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
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
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	1.5488	-	-	-	-	-
0.9746	12	-	0.7540	0.7565	0.7660	0.7176	0.7693
1.6244	20	0.674	-	-	-	-	-
1.9492	24	-	0.7622	0.7715	0.7781	0.7352	0.7790
2.4365	30	0.4592	-	-	-	-	-
2.9239	36	-	0.7648	0.7729	0.7778	0.7384	0.7799
3.2487	40	0.4113	-	-	-	-	-
3.8985	48	-	0.7657	0.7726	0.7771	0.7401	0.7810

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.0.1
Transformers: 4.42.4
PyTorch: 2.3.1+cu121
Accelerate: 0.32.1
Datasets: 2.20.0
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: 8 tokens mean: 46.43 tokens max: 439 tokens	min: 8 tokens mean: 20.76 tokens max: 43 tokens