rbhatia46/mxbai-embed-large-v1-financial-rag-matryoshka

Bmixedbread-ai/mxbai-embed-large-v1 Financial Matryoshka

This is a sentence-transformers model finetuned from mixedbread-ai/mxbai-embed-large-v1. It maps sentences & paragraphs to a 1024-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: mixedbread-ai/mxbai-embed-large-v1
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 1024 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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 1024, '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})
)

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("rbhatia46/mxbai-embed-large-v1-financial-rag-matryoshka")
# Run inference
sentences = [
    'In 2023, EnergyCorp declared a dividend of $2.5 per share.',
    'What dividend did the EnergyCorp pay to its shareholders in 2023?',
    'How did Amazon’s shift to one-day prime delivery affect its operational costs in 2023?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Information Retrieval

• Dataset: dim_1024
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.8923
cosine_accuracy@3	0.9692
cosine_accuracy@5	0.9692
cosine_accuracy@10	0.9846
cosine_precision@1	0.8923
cosine_precision@3	0.3231
cosine_precision@5	0.1938
cosine_precision@10	0.0985
cosine_recall@1	0.8923
cosine_recall@3	0.9692
cosine_recall@5	0.9692
cosine_recall@10	0.9846
cosine_ndcg@10	0.9419
cosine_mrr@10	0.9278
cosine_map@100	0.9281

Information Retrieval

• Dataset: dim_768
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.8923
cosine_accuracy@3	0.9692
cosine_accuracy@5	0.9692
cosine_accuracy@10	0.9846
cosine_precision@1	0.8923
cosine_precision@3	0.3231
cosine_precision@5	0.1938
cosine_precision@10	0.0985
cosine_recall@1	0.8923
cosine_recall@3	0.9692
cosine_recall@5	0.9692
cosine_recall@10	0.9846
cosine_ndcg@10	0.9423
cosine_mrr@10	0.9282
cosine_map@100	0.9284

Information Retrieval

• Dataset: dim_512
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.8923
cosine_accuracy@3	0.9692
cosine_accuracy@5	0.9692
cosine_accuracy@10	0.9846
cosine_precision@1	0.8923
cosine_precision@3	0.3231
cosine_precision@5	0.1938
cosine_precision@10	0.0985
cosine_recall@1	0.8923
cosine_recall@3	0.9692
cosine_recall@5	0.9692
cosine_recall@10	0.9846
cosine_ndcg@10	0.9419
cosine_mrr@10	0.9278
cosine_map@100	0.9281

Information Retrieval

• Dataset: dim_256
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.8923
cosine_accuracy@3	0.9692
cosine_accuracy@5	0.9692
cosine_accuracy@10	0.9846
cosine_precision@1	0.8923
cosine_precision@3	0.3231
cosine_precision@5	0.1938
cosine_precision@10	0.0985
cosine_recall@1	0.8923
cosine_recall@3	0.9692
cosine_recall@5	0.9692
cosine_recall@10	0.9846
cosine_ndcg@10	0.9417
cosine_mrr@10	0.9276
cosine_map@100	0.9279

Information Retrieval

• Dataset: dim_128
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.8462
cosine_accuracy@3	0.9538
cosine_accuracy@5	0.9692
cosine_accuracy@10	0.9846
cosine_precision@1	0.8462
cosine_precision@3	0.3179
cosine_precision@5	0.1938
cosine_precision@10	0.0985
cosine_recall@1	0.8462
cosine_recall@3	0.9538
cosine_recall@5	0.9692
cosine_recall@10	0.9846
cosine_ndcg@10	0.9222
cosine_mrr@10	0.9013
cosine_map@100	0.9016

Information Retrieval

• Dataset: dim_64
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.8154
cosine_accuracy@3	0.9692
cosine_accuracy@5	0.9846
cosine_accuracy@10	0.9846
cosine_precision@1	0.8154
cosine_precision@3	0.3231
cosine_precision@5	0.1969
cosine_precision@10	0.0985
cosine_recall@1	0.8154
cosine_recall@3	0.9692
cosine_recall@5	0.9846
cosine_recall@10	0.9846
cosine_ndcg@10	0.9124
cosine_mrr@10	0.8877
cosine_map@100	0.888

Training Details

Training Dataset

Unnamed Dataset

• Size: 580 training samples
• Columns: positive and anchor
• Approximate statistics based on the first 1000 samples:

  	positive	anchor
  type	string	string
  details	min: 16 tokens mean: 44.21 tokens max: 98 tokens	min: 9 tokens mean: 17.5 tokens max: 30 tokens

• Samples:

  positive	anchor
  For the fiscal year 2020, Microsoft Corporation reported a net income of $44.3 billion, showing a 13% increase from the previous year.	What was the net income of Microsoft Corporation for the fiscal year 2020?
  As of the latest financial report, Amazon has a current price to earnings ratio (P/E ratio) of 76.6.	What is Amazon's current P/E ratio according to their latest financial report?
  Microsoft Corporation posted an EBITDA (Earnings Before Interest, Taxes, Depreciation, and Amortization) margin of approximately 47% in 2021, showcasing strong profitability.	What was Microsoft Corporation's EBITDA margin in 2021?

• Loss: MatryoshkaLoss with these parameters:

  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          1024,
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  

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
• 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: 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	dim_1024_cosine_map@100	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
0.8421	1	0.9032	0.8846	0.9033	0.9109	0.8695	0.9186
1.6842	2	0.9121	0.8948	0.9174	0.9199	0.8777	0.9198
2.5263	3	0.9281	0.9013	0.9202	0.9281	0.8879	0.9204
3.3684	4	0.9281	0.9016	0.9279	0.9281	0.888	0.9284

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.10.6
• 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

@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}
}