This is a sentence-transformers model finetuned from BAAI/bge-base-en-v1.5 on the json dataset. 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 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- json

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("mbegerez/bge-base-financial-matryoshka")
# Run inference
sentences = [
    "Belonging - Build a Walmart for everyone: a diverse, equitable and inclusive company, where associates' ideas and opinions matter. We are focused on having an inclusive culture where everyone feels they belong. We publish our diversity representation twice yearly, and hold ourselves accountable to providing recurring culture, diversity, equity, and inclusion updates to senior leadership, including our President and CEO, and members of the Board of Directors. Of the approximately 2.1 million associates employed worldwide, 52% identify as women. In the U.S., 50% of the approximately 1.6 million associates identify as people of color. We review our processes regarding our commitment to fair-pay practices.",
    'How does Walmart support diversity, equity, and inclusion within its workforce?',
    'What was the net sales of the company in fiscal 2022?',
]
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 with these parameters:
json
1{ 2 "truncate_dim": 768 3}

Metric	Value
cosine_accuracy@1	0.7071
cosine_accuracy@3	0.8543
cosine_accuracy@5	0.89
cosine_accuracy@10	0.9286
cosine_precision@1	0.7071
cosine_precision@3	0.2848
cosine_precision@5	0.178
cosine_precision@10	0.0929
cosine_recall@1	0.7071
cosine_recall@3	0.8543
cosine_recall@5	0.89
cosine_recall@10	0.9286
cosine_ndcg@10	0.8212
cosine_mrr@10	0.7865
cosine_map@100	0.7895

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator with these parameters:
json
1{ 2 "truncate_dim": 512 3}

Metric	Value
cosine_accuracy@1	0.7129
cosine_accuracy@3	0.8571
cosine_accuracy@5	0.8843
cosine_accuracy@10	0.9243
cosine_precision@1	0.7129
cosine_precision@3	0.2857
cosine_precision@5	0.1769
cosine_precision@10	0.0924
cosine_recall@1	0.7129
cosine_recall@3	0.8571
cosine_recall@5	0.8843
cosine_recall@10	0.9243
cosine_ndcg@10	0.8226
cosine_mrr@10	0.7896
cosine_map@100	0.793

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator with these parameters:
json
1{ 2 "truncate_dim": 256 3}

Metric	Value
cosine_accuracy@1	0.71
cosine_accuracy@3	0.8486
cosine_accuracy@5	0.89
cosine_accuracy@10	0.9214
cosine_precision@1	0.71
cosine_precision@3	0.2829
cosine_precision@5	0.178
cosine_precision@10	0.0921
cosine_recall@1	0.71
cosine_recall@3	0.8486
cosine_recall@5	0.89
cosine_recall@10	0.9214
cosine_ndcg@10	0.8192
cosine_mrr@10	0.7859
cosine_map@100	0.7894

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator with these parameters:
json
1{ 2 "truncate_dim": 128 3}

Metric	Value
cosine_accuracy@1	0.6829
cosine_accuracy@3	0.83
cosine_accuracy@5	0.8671
cosine_accuracy@10	0.9143
cosine_precision@1	0.6829
cosine_precision@3	0.2767
cosine_precision@5	0.1734
cosine_precision@10	0.0914
cosine_recall@1	0.6829
cosine_recall@3	0.83
cosine_recall@5	0.8671
cosine_recall@10	0.9143
cosine_ndcg@10	0.8009
cosine_mrr@10	0.7644
cosine_map@100	0.7677

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator with these parameters:
json
1{ 2 "truncate_dim": 64 3}

Metric	Value
cosine_accuracy@1	0.65
cosine_accuracy@3	0.7929
cosine_accuracy@5	0.83
cosine_accuracy@10	0.8771
cosine_precision@1	0.65
cosine_precision@3	0.2643
cosine_precision@5	0.166
cosine_precision@10	0.0877
cosine_recall@1	0.65
cosine_recall@3	0.7929
cosine_recall@5	0.83
cosine_recall@10	0.8771
cosine_ndcg@10	0.765
cosine_mrr@10	0.729
cosine_map@100	0.7338

Training Details

Training Dataset

json

Dataset: json
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: 44.63 tokens
max: 301 tokens
min: 7 tokens
mean: 20.6 tokens
max: 45 tokens

Samples:

positive	anchor
`Some key challenges and trends addressed in the 'Trends and Uncertainties' section of the MD&A include material events such as the decreasing but continuous supply chain constraints, uneven demands, new technology adoptions, and a conservative customer spending environment within a mixed macroeconomic context.`	`What are some of the key challenges and developments that Hewlett Packard Enterprise highlighted in the 'Trends and Uncertainties' section of their MD&A for fiscal 2022?`
`Supply of Components Although most components essential to the Company’s business are generally available from multiple sources, certain components are currently obtained from single or limited sources.`	`From which sources does Apple obtain certain essential components?`
`The document lists PPD reinstatement premiums as showing an unfavorable (2) million U.S. dollars adjustment.`	`What were the reinstatement premiums related to PPD expenses for the year shown in the document?`

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}
tp_size: 0
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: None
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
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
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
use_liger_kernel: False
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	dim_768_cosine_ndcg@10	dim_512_cosine_ndcg@10	dim_256_cosine_ndcg@10	dim_128_cosine_ndcg@10	dim_64_cosine_ndcg@10
0.8122	10	25.1822	-	-	-	-	-
1.0	13	-	0.8089	0.8069	0.8069	0.7846	0.7410
1.5685	20	11.1815	-	-	-	-	-
2.0	26	-	0.8174	0.8224	0.8189	0.7988	0.7594
2.3249	30	7.2855	-	-	-	-	-
3.0	39	-	0.8205	0.8225	0.8195	0.7999	0.7636
3.0812	40	7.356	-	-	-	-	-
3.731	48	-	0.8212	0.8226	0.8192	0.8009	0.765

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.11.12
Sentence Transformers: 4.1.0
Transformers: 4.51.3
PyTorch: 2.6.0+cu124
Accelerate: 1.5.2
Datasets: 3.5.0
Tokenizers: 0.21.1

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: 44.63 tokens max: 301 tokens	min: 7 tokens mean: 20.6 tokens max: 45 tokens