You can deploy this model and start earning money today!

Abinaya/
snowflake-arctic-embed-financial-matryoshka

Name: snowflake-arctic-embed-financial-matryoshka
Availability: InStock

Views

No views yet

BGE base Financial Matryoshka

This is a sentence-transformers model finetuned from Snowflake/snowflake-arctic-embed-m-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: Snowflake/snowflake-arctic-embed-m-v1.5
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': False}) 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("Abinaya/snowflake-arctic-embed-financial-matryoshka")
# Run inference
sentences = [
    'As of December 31, 2023, Bank of America reported gross derivative assets and liabilities totaling $290.3 billion and $301.2 billion, respectively. After accounting for legally enforceable master netting agreements and cash collateral, these figures were adjusted to $39.3 billion in assets and $43.4 billion in liabilities.',
    'What were the total derivative assets and liabilities at Bank of America as of December 31, 2023, after adjusting for master netting agreements and cash collateral?',
    'By what percentage did HIV product sales increase in 2023 compared to the previous year?',
]
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

Datasets: dim_768, dim_512, dim_256, dim_128 and dim_64
Evaluated with InformationRetrievalEvaluator

Metric	dim_768	dim_512	dim_256	dim_128	dim_64
cosine_accuracy@1	0.7543	0.7543	0.7557	0.7443	0.7
cosine_accuracy@3	0.8614	0.8614	0.86	0.85	0.8157
cosine_accuracy@5	0.8914	0.8914	0.8943	0.8871	0.8571
cosine_accuracy@10	0.9329	0.93	0.9286	0.9143	0.9071
cosine_precision@1	0.7543	0.7543	0.7557	0.7443	0.7
cosine_precision@3	0.2871	0.2871	0.2867	0.2833	0.2719
cosine_precision@5	0.1783	0.1783	0.1789	0.1774	0.1714
cosine_precision@10	0.0933	0.093	0.0929	0.0914	0.0907
cosine_recall@1	0.7543	0.7543	0.7557	0.7443	0.7
cosine_recall@3	0.8614	0.8614	0.86	0.85	0.8157
cosine_recall@5	0.8914	0.8914	0.8943	0.8871	0.8571
cosine_recall@10	0.9329	0.93	0.9286	0.9143	0.9071
cosine_ndcg@10	0.8431	0.8409	0.8409	0.8298	0.8023
cosine_mrr@10	0.8144	0.8124	0.8129	0.8026	0.7689
cosine_map@100	0.8171	0.8153	0.8158	0.8061	0.7722

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: 46.81 tokens
max: 326 tokens
min: 9 tokens
mean: 20.23 tokens
max: 45 tokens

Samples:

positive	anchor
Opioids Related Securities Class Actions and Derivative Litigation: Three derivative complaints and two securities class actions drawing heavily on the allegations of the DOJ complaint have been filed in Delaware naming the Company and various current and former directors and certain current and former officers as defendants. The plaintiffs in the derivative suits (in which the Company is a nominal defendant) allege, among other things, that the defendants breached their fidariety duties in connection with oversight of opioids dispensing and distribution and that the defendants violated Section 14(a) of the Securities Exchange Act of 1934, as amended (the 'Exchange Act()), and are liable for contribution under Section 10(b) of the Exchange Act in connection with the Company's disclosures about opioids.	`What kind of claims are involved in the securities and derivative litigation against the Company listed in the document?`
`Walmart's fintech venture, ONE, provides financial services such as money orders, prepaid access, money transfers, check cashing, bill payment, and certain types of installment lending.`	`What types of financial services are offered through Walmart's fintech venture, ONE?`
`Juice and juice concentrate from various fruits, particularly orange juice and orange juice concentrate, are principal raw materials for juice and juice drink products, and milk is the principal raw material for dairy products managed through fairlife, LLC.`	`What are the primary raw materials for the company's juice and dairy products?`

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: 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
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
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	1.5521	-	-	-	-	-
1.0	13	-	0.8136	0.8108	0.8143	0.7949	0.7552
1.5685	20	0.4812	-	-	-	-	-
2.0	26	-	0.8405	0.8388	0.8384	0.8284	0.7990
2.3249	30	0.3585	-	-	-	-	-
3.0	39	-	0.8420	0.8409	0.8408	0.8290	0.8009
3.0812	40	0.3101	-	-	-	-	-
3.731	48	-	0.8431	0.8409	0.8409	0.8298	0.8023

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.15
Sentence Transformers: 3.4.0
Transformers: 4.47.1
PyTorch: 2.5.1+cu124
Accelerate: 1.0.1
Datasets: 3.0.1
Tokenizers: 0.21.0

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: 46.81 tokens max: 326 tokens	min: 9 tokens mean: 20.23 tokens max: 45 tokens