Nomic Financial Matryoshka
This is a sentence-transformers model finetuned from nomic-ai/nomic-embed-text-v1 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
- Maximum Sequence Length: 8192 tokens
- Output Dimensionality: 768 tokens
- 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})
(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.
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("aniket0898/bge-base-financial-matryoshka")
# Run inference
sentences = [
'How is the information about legal proceedings organized in the financial documents according to the provided context?',
"The information about legal proceedings is organized under Part II, Item 8 in the section titled 'Financial Statements and Supplementary Data – Note 14'.",
'We have a match-funding policy that addresses the interest rate risk by aligning the interest rate profile (fixed or floating rate and duration) of our debt portfolio with the interest rate profile of our finance receivable portfolio within a predetermined range on an ongoing basis. In connection with that policy, we use interest rate derivative instruments to modify the debt structure to match assets within the finance receivable portfolio.',
]
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 | 0.7457 |
cosine_accuracy@3 | 0.8614 |
cosine_accuracy@5 | 0.8957 |
cosine_accuracy@10 | 0.93 |
cosine_precision@1 | 0.7457 |
cosine_precision@3 | 0.2871 |
cosine_precision@5 | 0.1791 |
cosine_precision@10 | 0.093 |
cosine_recall@1 | 0.7457 |
cosine_recall@3 | 0.8614 |
cosine_recall@5 | 0.8957 |
cosine_recall@10 | 0.93 |
cosine_ndcg@10 | 0.8399 |
cosine_mrr@10 | 0.8108 |
cosine_map@100 | 0.8137 |
Information Retrieval
- Dataset:
dim_512
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.7357 |
cosine_accuracy@3 | 0.8514 |
cosine_accuracy@5 | 0.8914 |
cosine_accuracy@10 | 0.93 |
cosine_precision@1 | 0.7357 |
cosine_precision@3 | 0.2838 |
cosine_precision@5 | 0.1783 |
cosine_precision@10 | 0.093 |
cosine_recall@1 | 0.7357 |
cosine_recall@3 | 0.8514 |
cosine_recall@5 | 0.8914 |
cosine_recall@10 | 0.93 |
cosine_ndcg@10 | 0.8353 |
cosine_mrr@10 | 0.8047 |
cosine_map@100 | 0.8075 |
Information Retrieval
- Dataset:
dim_256
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.7286 |
cosine_accuracy@3 | 0.8614 |
cosine_accuracy@5 | 0.8857 |
cosine_accuracy@10 | 0.9271 |
cosine_precision@1 | 0.7286 |
cosine_precision@3 | 0.2871 |
cosine_precision@5 | 0.1771 |
cosine_precision@10 | 0.0927 |
cosine_recall@1 | 0.7286 |
cosine_recall@3 | 0.8614 |
cosine_recall@5 | 0.8857 |
cosine_recall@10 | 0.9271 |
cosine_ndcg@10 | 0.832 |
cosine_mrr@10 | 0.8011 |
cosine_map@100 | 0.8041 |
Information Retrieval
- Dataset:
dim_128
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.7129 |
cosine_accuracy@3 | 0.8329 |
cosine_accuracy@5 | 0.8671 |
cosine_accuracy@10 | 0.9143 |
cosine_precision@1 | 0.7129 |
cosine_precision@3 | 0.2776 |
cosine_precision@5 | 0.1734 |
cosine_precision@10 | 0.0914 |
cosine_recall@1 | 0.7129 |
cosine_recall@3 | 0.8329 |
cosine_recall@5 | 0.8671 |
cosine_recall@10 | 0.9143 |
cosine_ndcg@10 | 0.8146 |
cosine_mrr@10 | 0.7826 |
cosine_map@100 | 0.786 |
Information Retrieval
- Dataset:
dim_64
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.6643 |
cosine_accuracy@3 | 0.8043 |
cosine_accuracy@5 | 0.8457 |
cosine_accuracy@10 | 0.9029 |
cosine_precision@1 | 0.6643 |
cosine_precision@3 | 0.2681 |
cosine_precision@5 | 0.1691 |
cosine_precision@10 | 0.0903 |
cosine_recall@1 | 0.6643 |
cosine_recall@3 | 0.8043 |
cosine_recall@5 | 0.8457 |
cosine_recall@10 | 0.9029 |
cosine_ndcg@10 | 0.7821 |
cosine_mrr@10 | 0.7437 |
cosine_map@100 | 0.7468 |
Training Details
Training Dataset
json
- Dataset: json
- Size: 6,300 training samples
- Columns:
anchor
andpositive
- Approximate statistics based on the first 1000 samples:
anchor positive type string string details - min: 2 tokens
- mean: 20.47 tokens
- max: 40 tokens
- min: 9 tokens
- mean: 45.09 tokens
- max: 272 tokens
- Samples:
anchor positive What was the stored value of cards and loyalty program balances at the end of fiscal year 2022?
Stored value cards and loyalty program at October 2, 2022 showed a balance of approximately $1.503 billion.
What transformation is planned for Le Jardin located at The Londoner Macao?
Le Jardin, located on the southern flank of The Londoner Macao, is to undergo a transformation into a distinctive garden-themed attraction spanning approximately 50,000 square meters.
What are the key terms of the new Labor Agreement ratified by the UAW in 2023?
The key terms and provisions of the Labor Agreement are: General wage increases of 11% upon ratification in 2023, 3% in September each of 2024, 2025 and 2026, and 5% in September 2027; Consolidation of applicable wage classifications for in-progression, temporary and other employees – with employees reaching the top classification rate upon the completion of 156 weeks of active service; The re-establishment of a cost-of-living allowance; Lump sum ratification bonus payments of $5,000 paid to eligible employees in the three months ended December 31, 2023; For members currently employed and enrolled in the Employees’ Pension Plan, an increase of $5.00 to the monthly basic benefit for past and future service provided; A 3.6% increase in company contributions to eligible employees' defined contribution retirement accounts; and Annual contribution of $500 to eligible retirees or surviving spouses.
- 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
: epochper_device_train_batch_size
: 32per_device_eval_batch_size
: 16gradient_accumulation_steps
: 16learning_rate
: 2e-05num_train_epochs
: 4lr_scheduler_type
: cosinewarmup_ratio
: 0.1bf16
: Truetf32
: Trueload_best_model_at_end
: Trueoptim
: adamw_torch_fusedbatch_sampler
: no_duplicates
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: epochprediction_loss_only
: Trueper_device_train_batch_size
: 32per_device_eval_batch_size
: 16per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 16eval_accumulation_steps
: Nonelearning_rate
: 2e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 4max_steps
: -1lr_scheduler_type
: cosinelr_scheduler_kwargs
: {}warmup_ratio
: 0.1warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Truefp16
: Falsefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Truelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Trueignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torch_fusedoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Falsehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseeval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falsebatch_sampler
: no_duplicatesmulti_dataset_batch_sampler
: proportional
Training Logs
Epoch | Step | Training Loss | dim_768_cosine_map@100 | dim_512_cosine_map@100 | dim_256_cosine_map@100 | dim_128_cosine_map@100 | dim_64_cosine_map@100 |
---|---|---|---|---|---|---|---|
0.8122 | 10 | 0.7331 | - | - | - | - | - |
0.9746 | 12 | - | 0.7871 | 0.7796 | 0.7747 | 0.7546 | 0.7214 |
1.6244 | 20 | 0.2506 | - | - | - | - | - |
1.9492 | 24 | - | 0.8021 | 0.7990 | 0.7869 | 0.7691 | 0.7371 |
2.4365 | 30 | 0.1029 | - | - | - | - | - |
2.9239 | 36 | - | 0.8030 | 0.8017 | 0.7926 | 0.7760 | 0.7402 |
3.2487 | 40 | 0.054 | - | - | - | - | - |
3.8985 | 48 | - | 0.8055 | 0.799 | 0.7924 | 0.7754 | 0.7383 |
0.8122 | 10 | 0.0397 | - | - | - | - | - |
0.9746 | 12 | - | 0.8109 | 0.7983 | 0.7974 | 0.7795 | 0.7373 |
1.6244 | 20 | 0.0301 | - | - | - | - | - |
1.9492 | 24 | - | 0.8115 | 0.8049 | 0.8026 | 0.7839 | 0.7486 |
2.4365 | 30 | 0.0236 | - | - | - | - | - |
2.9239 | 36 | - | 0.8138 | 0.8082 | 0.8045 | 0.7858 | 0.7470 |
3.2487 | 40 | 0.0131 | - | - | - | - | - |
3.8985 | 48 | - | 0.8137 | 0.8075 | 0.8041 | 0.786 | 0.7468 |
- The bold row denotes the saved checkpoint.
Framework Versions
- Python: 3.8.10
- Sentence Transformers: 3.2.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 1.0.1
- Datasets: 2.19.1
- Tokenizers: 0.19.1
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}
}
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Base model
nomic-ai/nomic-embed-text-v1Evaluation results
- Cosine Accuracy@1 on dim 768self-reported0.746
- Cosine Accuracy@3 on dim 768self-reported0.861
- Cosine Accuracy@5 on dim 768self-reported0.896
- Cosine Accuracy@10 on dim 768self-reported0.930
- Cosine Precision@1 on dim 768self-reported0.746
- Cosine Precision@3 on dim 768self-reported0.287
- Cosine Precision@5 on dim 768self-reported0.179
- Cosine Precision@10 on dim 768self-reported0.093
- Cosine Recall@1 on dim 768self-reported0.746
- Cosine Recall@3 on dim 768self-reported0.861