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RK-1235/
bge-base-FIR-matryoshka-BASELINE-10epochs-FT

Name: bge-base-FIR-matryoshka-BASELINE-10epochs-FT
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BGE base Financial Matryoshka

This is a sentence-transformers model finetuned from BAAI/bge-base-en 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
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- json
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("RK-1235/bge-base-FIR-matryoshka-BASELINE-10epochs-FT")
# Run inference
sentences = [
    'Item 8. Financial Statements and Supplementary Data. The Consolidated Financial Statements, together with the Notes thereto and the report thereon dated February 16, 2024, of PricewaterhouseCoopers LLP, the Firm’s independent registered public accounting firm (PCAOB ID 238).',
    'What type of data does Item 8 in a financial document contain?',
    "How did the assumptions and estimates used for assessing the fair value of reporting units potentially impact the company's financial statements?",
]
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	0.2041
cosine_accuracy@3	0.3908
cosine_accuracy@5	0.4557
cosine_accuracy@10	0.5427
cosine_precision@1	0.2041
cosine_precision@3	0.1303
cosine_precision@5	0.0911
cosine_precision@10	0.0543
cosine_recall@1	0.2041
cosine_recall@3	0.3908
cosine_recall@5	0.4557
cosine_recall@10	0.5427
cosine_ndcg@10	0.3713
cosine_mrr@10	0.3167
cosine_map@100	0.3257

Information Retrieval

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

Metric	Value
cosine_accuracy@1	0.1788
cosine_accuracy@3	0.3845
cosine_accuracy@5	0.4494
cosine_accuracy@10	0.5222
cosine_precision@1	0.1788
cosine_precision@3	0.1282
cosine_precision@5	0.0899
cosine_precision@10	0.0522
cosine_recall@1	0.1788
cosine_recall@3	0.3845
cosine_recall@5	0.4494
cosine_recall@10	0.5222
cosine_ndcg@10	0.3521
cosine_mrr@10	0.2975
cosine_map@100	0.3072

Information Retrieval

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

Metric	Value
cosine_accuracy@1	0.1756
cosine_accuracy@3	0.3386
cosine_accuracy@5	0.3924
cosine_accuracy@10	0.4968
cosine_precision@1	0.1756
cosine_precision@3	0.1129
cosine_precision@5	0.0785
cosine_precision@10	0.0497
cosine_recall@1	0.1756
cosine_recall@3	0.3386
cosine_recall@5	0.3924
cosine_recall@10	0.4968
cosine_ndcg@10	0.3278
cosine_mrr@10	0.2749
cosine_map@100	0.284

Information Retrieval

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

Metric	Value
cosine_accuracy@1	0.1345
cosine_accuracy@3	0.2769
cosine_accuracy@5	0.3434
cosine_accuracy@10	0.4019
cosine_precision@1	0.1345
cosine_precision@3	0.0923
cosine_precision@5	0.0687
cosine_precision@10	0.0402
cosine_recall@1	0.1345
cosine_recall@3	0.2769
cosine_recall@5	0.3434
cosine_recall@10	0.4019
cosine_ndcg@10	0.2643
cosine_mrr@10	0.2206
cosine_map@100	0.2315

Information Retrieval

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

Metric	Value
cosine_accuracy@1	0.0854
cosine_accuracy@3	0.1946
cosine_accuracy@5	0.2484
cosine_accuracy@10	0.3165
cosine_precision@1	0.0854
cosine_precision@3	0.0649
cosine_precision@5	0.0497
cosine_precision@10	0.0316
cosine_recall@1	0.0854
cosine_recall@3	0.1946
cosine_recall@5	0.2484
cosine_recall@10	0.3165
cosine_ndcg@10	0.1936
cosine_mrr@10	0.1553
cosine_map@100	0.1641

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: 46.06 tokens
max: 371 tokens
min: 8 tokens
mean: 20.8 tokens
max: 51 tokens

Samples:

positive	anchor
`As of December 31, 2023, a 5 percent change in the contingent consideration liabilities would result in a change in income before income taxes of $5.2 million.`	`How would a 5% change in the contingent consideration liabilities impact income before taxes as of December 31, 2023?`
`NIKE, Inc.'s principal business activity involves the design, development, and worldwide marketing and selling of athletic footwear, apparel, equipment, accessories, and services.`	`What is the principal business activity of NIKE, Inc.?`
`During 2023, changes in foreign currencies relative to the U.S. dollar negatively impacted net sales by approximately $3,484, 156 basis points, compared to 2022, attributable to our Canadian and Other International operations.`	`What was the overall impact of foreign currencies on net sales in 2023?`

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: 10
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: 10
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: 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	89.0763	-	-	-	-	-
1.0	13	-	0.4022	0.3835	0.3505	0.2911	0.1835
1.5685	20	36.7538	-	-	-	-	-
2.0	26	-	0.3725	0.3591	0.3218	0.2753	0.1978
2.3249	30	17.7869	-	-	-	-	-
3.0	39	-	0.3680	0.3558	0.3284	0.2638	0.2000
3.0812	40	10.5904	-	-	-	-	-
3.8934	50	7.9568	-	-	-	-	-
4.0	52	-	0.3634	0.3487	0.3245	0.2589	0.1999
4.6497	60	5.5002	-	-	-	-	-
5.0	65	-	0.3648	0.3551	0.3211	0.2595	0.1968
5.4061	70	5.3314	-	-	-	-	-
6.0	78	-	0.3693	0.3548	0.3257	0.2621	0.1977
6.1624	80	4.6165	-	-	-	-	-
6.9746	90	4.7811	-	-	-	-	-
7.0	91	-	0.3698	0.3532	0.3293	0.2637	0.1954
7.7310	100	3.978	-	-	-	-	-
8.0	104	-	0.3713	0.3523	0.3273	0.2637	0.1952
8.4873	110	4.1624	-	-	-	-	-
9.0	117	-	0.3707	0.3517	0.3264	0.2639	0.1949
9.2437	120	3.4956	-	-	-	-	-
10.0	130	3.9661	0.3713	0.3521	0.3278	0.2643	0.1936

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.12
Sentence Transformers: 4.1.0
Transformers: 4.52.2
PyTorch: 2.6.0+cu124
Accelerate: 1.7.0
Datasets: 3.6.0
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: 46.06 tokens max: 371 tokens	min: 8 tokens mean: 20.8 tokens max: 51 tokens