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venkateshmurugadas/
nomic-v1.5-financial-matryoshka

Name: nomic-v1.5-financial-matryoshka
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Nomic Embed 1.5 Financial Matryoshka

This is a sentence-transformers model finetuned from nomic-ai/nomic-embed-text-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: nomic-ai/nomic-embed-text-v1.5
Maximum Sequence Length: 8192 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': 8192, 'do_lower_case': False}) with Transformer model: NomicBertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

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("venkateshmurugadas/nomic-v1.5-financial-matryoshka")
# Run inference
sentences = [
    'The company may issue debt or equity securities occasionally to provide additional liquidity or pursue opportunities to enhance its long-term competitive position while maintaining a strong balance sheet. ',
    'What might the company do to increase liquidity or pursue long-term competitive advantages while managing a strong balance sheet?',
    'What types of technologies does the Mortgage Technology segment employ to enhance operational efficiency?',
]
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	0.6929
cosine_accuracy@3	0.8229
cosine_accuracy@5	0.87
cosine_accuracy@10	0.9071
cosine_precision@1	0.6929
cosine_precision@3	0.2743
cosine_precision@5	0.174
cosine_precision@10	0.0907
cosine_recall@1	0.6929
cosine_recall@3	0.8229
cosine_recall@5	0.87
cosine_recall@10	0.9071
cosine_ndcg@10	0.803
cosine_mrr@10	0.7693
cosine_map@100	0.7724

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6914
cosine_accuracy@3	0.8271
cosine_accuracy@5	0.87
cosine_accuracy@10	0.9086
cosine_precision@1	0.6914
cosine_precision@3	0.2757
cosine_precision@5	0.174
cosine_precision@10	0.0909
cosine_recall@1	0.6914
cosine_recall@3	0.8271
cosine_recall@5	0.87
cosine_recall@10	0.9086
cosine_ndcg@10	0.803
cosine_mrr@10	0.7688
cosine_map@100	0.7718

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6871
cosine_accuracy@3	0.8286
cosine_accuracy@5	0.8729
cosine_accuracy@10	0.8986
cosine_precision@1	0.6871
cosine_precision@3	0.2762
cosine_precision@5	0.1746
cosine_precision@10	0.0899
cosine_recall@1	0.6871
cosine_recall@3	0.8286
cosine_recall@5	0.8729
cosine_recall@10	0.8986
cosine_ndcg@10	0.7984
cosine_mrr@10	0.7656
cosine_map@100	0.7693

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6671
cosine_accuracy@3	0.8186
cosine_accuracy@5	0.8557
cosine_accuracy@10	0.8957
cosine_precision@1	0.6671
cosine_precision@3	0.2729
cosine_precision@5	0.1711
cosine_precision@10	0.0896
cosine_recall@1	0.6671
cosine_recall@3	0.8186
cosine_recall@5	0.8557
cosine_recall@10	0.8957
cosine_ndcg@10	0.785
cosine_mrr@10	0.7491
cosine_map@100	0.7525

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6529
cosine_accuracy@3	0.7871
cosine_accuracy@5	0.8271
cosine_accuracy@10	0.8771
cosine_precision@1	0.6529
cosine_precision@3	0.2624
cosine_precision@5	0.1654
cosine_precision@10	0.0877
cosine_recall@1	0.6529
cosine_recall@3	0.7871
cosine_recall@5	0.8271
cosine_recall@10	0.8771
cosine_ndcg@10	0.764
cosine_mrr@10	0.728
cosine_map@100	0.7318

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: 46.46 tokens
max: 371 tokens
min: 2 tokens
mean: 20.45 tokens
max: 41 tokens

Samples:

positive	anchor
`We evaluate uncertain tax positions periodically, considering changes in facts and circumstances, such as new regulations or recent judicial opinions, as well as the status of audit activities by taxing authorities.`	`How are changes to a company's uncertain tax positions evaluated?`
`During 2022 and 2023, our operating margin was impacted by increased wage rates. During 2022, our gross margin was impacted by higher air freight costs as a result of global supply chain disruption.`	`What effects did inflation have on the company's operating results during 2022 and 2023?`
To mitigate these developments, we are continually working to evolve our advertising systems to improve the performance of our ad products. We are developing privacy enhancing technologies to deliver relevant ads and measurement capabilities while reducing the amount of personal information we process, including by relying more on anonymized or aggregated third-party data. In addition, we are developing tools that enable marketers to share their data into our systems, as well as ad products that generate more valuable signals within our apps. More broadly, we also continue to innovate our advertising tools to help marketers prepare campaigns and connect with consumers, including developing growing formats such as Reels ads and our business messaging ad products. Across all of these efforts, we are making significant investments in artificial intelligence (AI), including generative AI, to improve our delivery, targeting, and measurement capabilities. Further, we are focused on driving onsite conversions in our business messaging ad products by developing new features and scaling existing features.	`What technological solutions is the company developing to improve ad delivery?`

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: 4
per_device_eval_batch_size: 4
gradient_accumulation_steps: 64
learning_rate: 2e-05
num_train_epochs: 4
lr_scheduler_type: cosine
warmup_ratio: 0.1
fp16: True
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: 4
per_device_eval_batch_size: 4
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 64
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: True
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	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
0.4063	10	0.1329	-	-	-	-	-
0.8127	20	0.0567	-	-	-	-	-
0.9752	24	-	0.7416	0.7604	0.7678	0.7249	0.7758
1.2190	30	0.0415	-	-	-	-	-
1.6254	40	0.0043	-	-	-	-	-
1.9911	49	-	0.7491	0.7648	0.7700	0.7315	0.7731
2.0317	50	0.0059	-	-	-	-	-
2.4381	60	0.0045	-	-	-	-	-
2.8444	70	0.0013	-	-	-	-	-
2.9663	73	-	0.7531	0.7703	0.7712	0.7327	0.7738
3.2508	80	0.0031	-	-	-	-	-
3.6571	90	0.0009	-	-	-	-	-
3.9010	96	-	0.7525	0.7693	0.7718	0.7318	0.7724

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: 0.31.0
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}
}

	positive	anchor
type	string	string
details	min: 8 tokens mean: 46.46 tokens max: 371 tokens	min: 2 tokens mean: 20.45 tokens max: 41 tokens