shail-2512/nomic-embed-financial-matryoshka

Nomic Embed Financial Matryoshka

This is a sentence-transformers model finetuned from nomic-ai/nomic-embed-text-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: nomic-ai/nomic-embed-text-v1.5
• Maximum Sequence Length: 8192 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': 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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("shail-2512/nomic-embed-financial-matryoshka")
# Run inference
sentences = [
    'How are government incentives treated in accounting according to the given information?',
    'We are entitled to certain advanced manufacturing production credits under the IRA, and government incentives are not accounted for or classified as an income tax credit. We account for government incentives as a reduction of expense, a reduction of the cost of the capital investment or other income based on the substance of the incentive received. Benefits are generally recorded when there is reasonable assurance of receipt or, as it relates with advanced manufacturing production credits, upon the generation of the credit.',
    'Basic net income per share is computed by dividing net income attributable to common stock by the weighted-average number of shares of common stock outstanding during the period.',
]
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	0.7186	0.7157	0.7029	0.7	0.69
cosine_accuracy@3	0.87	0.8686	0.86	0.8429	0.83
cosine_accuracy@5	0.9014	0.9029	0.8914	0.8771	0.8671
cosine_accuracy@10	0.9357	0.9343	0.9271	0.9271	0.9129
cosine_precision@1	0.7186	0.7157	0.7029	0.7	0.69
cosine_precision@3	0.29	0.2895	0.2867	0.281	0.2767
cosine_precision@5	0.1803	0.1806	0.1783	0.1754	0.1734
cosine_precision@10	0.0936	0.0934	0.0927	0.0927	0.0913
cosine_recall@1	0.7186	0.7157	0.7029	0.7	0.69
cosine_recall@3	0.87	0.8686	0.86	0.8429	0.83
cosine_recall@5	0.9014	0.9029	0.8914	0.8771	0.8671
cosine_recall@10	0.9357	0.9343	0.9271	0.9271	0.9129
cosine_ndcg@10	0.8338	0.8321	0.8208	0.8175	0.8043
cosine_mrr@10	0.8005	0.7986	0.7862	0.7821	0.7693
cosine_map@100	0.8031	0.8013	0.7893	0.7853	0.7729

Training Details

Training Dataset

json

• Dataset: json
• Size: 6,300 training samples
• Columns: anchor and positive
• Approximate statistics based on the first 1000 samples:

  	anchor	positive
  type	string	string
  details	min: 2 tokens mean: 20.65 tokens max: 45 tokens	min: 2 tokens mean: 46.29 tokens max: 326 tokens

• Samples:

  anchor	positive
  Where is the Investor Relations office of Intuit Inc. located?	Copies of this Annual Report on Form 10-K may also be obtained without charge by contacting Investor Relations, Intuit Inc., P.O. Box 7850, Mountain View, California 94039-7850, calling 650-944-6000, or emailing [email protected].
  Where is the Financial Statement Schedule located in the Form 10-K?	The Financial Statement Schedule is found on page S-1 of the Form 10-K.
  What factors are considered when evaluating the realization of deferred tax assets?	Many factors are considered when assessing whether it is more likely than not that the deferred tax assets will be realized, including recent cumulative earnings, expectations of future taxable income, carryforward periods and other relevant quantitative and qualitative factors.

• Loss: MatryoshkaLoss with these parameters:

  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  

Evaluation Dataset

json

• Dataset: json
• Size: 700 evaluation samples
• Columns: anchor and positive
• Approximate statistics based on the first 700 samples:

  	anchor	positive
  type	string	string
  details	min: 2 tokens mean: 20.71 tokens max: 45 tokens	min: 9 tokens mean: 46.74 tokens max: 248 tokens

• Samples:

  anchor	positive
  What fiscal changes did Garmin make in January 2023?	The Company announced an organization realignment in January 2023, which combined the consumer auto operating segment with the outdoor operating segment.
  Where are the details about 'Legal Matters' and 'Government Investigations, Audits and Reviews' located in the financial statements?	The information required by this Item 3 is incorporated herein by reference to the information set forth under the captions 'Legal Matters' and 'Government Investigations, Audits and Reviews' in Note 12 of the Notes to the Consolidated Financial Statements included in Part II, Item 8, 'Financial Statements and Supplementary Data'.
  Are the pages of IBM's Management’s Discussion and Analysis section in the 2023 Annual Report included in the report itself?	In IBM’s 2023 Annual Report, the pages containing Management’s Discussion and Analysis of Financial Condition and Results of Operations (pages 6 through 40) are incorporated by reference.

• Loss: MatryoshkaLoss with these parameters:

  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: epoch
• gradient_accumulation_steps: 8
• learning_rate: 2e-05
• lr_scheduler_type: cosine
• warmup_ratio: 0.1
• bf16: 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: 8
• per_device_eval_batch_size: 8
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 8
• 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: 3
• 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: None
• 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	Validation 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
0.1015	10	0.2626	-	-	-	-	-	-
0.2030	20	0.1764	-	-	-	-	-	-
0.1015	10	0.0311	-	-	-	-	-	-
0.2030	20	0.0259	-	-	-	-	-	-
0.1015	10	0.0056	-	-	-	-	-	-
0.2030	20	0.0064	-	-	-	-	-	-
0.1015	10	0.0016	-	-	-	-	-	-
0.2030	20	0.0015	-	-	-	-	-	-
0.1015	10	0.0006	-	-	-	-	-	-
0.2030	20	0.0006	-	-	-	-	-	-
0.3046	30	0.1324	-	-	-	-	-	-
0.4061	40	0.113	-	-	-	-	-	-
0.5076	50	0.128	-	-	-	-	-	-
0.6091	60	0.1134	-	-	-	-	-	-
0.7107	70	0.056	-	-	-	-	-	-
0.8122	80	0.1086	-	-	-	-	-	-
0.9137	90	0.1008	-	-	-	-	-	-
1.0	99	-	0.0771	0.8286	0.8306	0.8266	0.8197	0.7955
1.0102	100	0.0491	-	-	-	-	-	-
1.1117	110	0.0029	-	-	-	-	-	-
1.2132	120	0.0009	-	-	-	-	-	-
1.3147	130	0.0326	-	-	-	-	-	-
1.4162	140	0.0077	-	-	-	-	-	-
1.5178	150	0.0109	-	-	-	-	-	-
1.6193	160	0.0047	-	-	-	-	-	-
1.7208	170	0.004	-	-	-	-	-	-
1.8223	180	0.0122	-	-	-	-	-	-
1.9239	190	0.0043	-	-	-	-	-	-
2.0	198	-	0.0758	0.8296	0.8330	0.8222	0.8169	0.7998
2.0203	200	0.0032	-	-	-	-	-	-
2.1218	210	0.0002	-	-	-	-	-	-
2.2234	220	0.0002	-	-	-	-	-	-
2.3249	230	0.0097	-	-	-	-	-	-
2.4264	240	0.0012	-	-	-	-	-	-
2.5279	250	0.0012	-	-	-	-	-	-
2.6294	260	0.0009	-	-	-	-	-	-
2.7310	270	0.0007	-	-	-	-	-	-
2.8325	280	0.0019	-	-	-	-	-	-
2.9340	290	0.0009	-	-	-	-	-	-
2.9746	294	-	0.0744	0.8338	0.8321	0.8208	0.8175	0.8043

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 3.3.1
• Transformers: 4.47.0
• PyTorch: 2.5.1+cu121
• Accelerate: 1.1.1
• Datasets: 3.1.0
• Tokenizers: 0.21.0

Citation

BibTeX

Sentence Transformers

@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

@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

@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}
}