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llm-semantic-router/mmbert-embed-finance

sourceHugging Faceupdated 7mo agoView on Hugging Face
3likes88downloads
Model Card

SentenceTransformer based on llm-semantic-router/mmbert-embed-32k-2d-matryoshka

This is a sentence-transformers model finetuned from llm-semantic-router/mmbert-embed-32k-2d-matryoshka. 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: llm-semantic-router/mmbert-embed-32k-2d-matryoshka <!-- at revision 38ba9e6c90ad4f7631d7402c5bc2c0f953277aae -->
  • —Maximum Sequence Length: 32768 tokens
  • —Output Dimensionality: 768 dimensions
  • —Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown -->

Model Sources

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 32768, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
  (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:

bash
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'What is included in Item 8 of the document?',
    'What is included in Item 8 of the document?\n\nAnswer: Financial Statements and Supplementary Data',
    'What is the content of Item 8 in the document?\n\nAnswer: Item 8 of the document includes Financial Statements and Supplementary Data.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0078, 0.9297, 0.5508],
#         [0.9297, 0.9922, 0.6680],
#         [0.5508, 0.6680, 1.0000]], dtype=torch.bfloat16)

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Training Details

Training Dataset

Unnamed Dataset
  • —Size: 606 training samples
  • —Columns: <code>sentence0</code>, <code>sentence1</code>, and <code>sentence_2</code>
  • —Approximate statistics based on the first 606 samples: | | sentence0 | sentence1 | sentence_2 | |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 10 tokens</li><li>mean: 22.46 tokens</li><li>max: 55 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 52.15 tokens</li><li>max: 229 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 49.13 tokens</li><li>max: 120 tokens</li></ul> |
  • —Samples: | sentence0 | sentence1 | sentence_2 | |:-------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>What led to the increase in operating income margin for the Dollar Tree segment in 2022?</code> | <code>What led to the increase in operating income margin for the Dollar Tree segment in 2022?<br><br>Answer: The increase in operating income margin for the Dollar Tree segment in 2022 was primarily due to the gross profit margin increase and a decrease in the selling, general and administrative expense rate.</code> | <code>What was the increase in the gross profit margin for the fiscal year 2022 compared to the previous year?<br><br>Answer: The increase in the gross profit margin for the fiscal year 2022 compared to the previous year was 2.1%.</code> | | <code>How much net cash was provided by operating activities in 2022?</code> | <code>How much net cash was provided by operating activities in 2022?<br><br>Answer: $4.5 billion</code> | <code>What were the main components contributing to the net cash provided by operating activities in 2023?<br><br>Answer: Operating income, depreciation and amortization, and various adjustments in operating assets and liabilities were the main contributors.</code> | | <code>How was the stock-based compensation expense of $254 million accounted for in the company's financial statements?</code> | <code>How was the stock-based compensation expense of $254 million accounted for in the company's financial statements?<br><br>Answer: The stock-based compensation expense of $254 million was recorded in the line item selling, general, and administrative expenses in the company's consolidated statement of income.</code> | <code>How much did the cost of revenue increase in stock-based compensation expense from fiscal year 2021 to 2023?<br><br>Answer: $2 million</code> |
  • —Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:
json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false
  }

Training Hyperparameters

Non-Default Hyperparameters
  • —num_train_epochs: 2
  • —multi_dataset_batch_sampler: round_robin
All Hyperparameters

<details><summary>Click to expand</summary>

  • —do_predict: False
  • —eval_strategy: no
  • —prediction_loss_only: True
  • —per_device_train_batch_size: 8
  • —per_device_eval_batch_size: 8
  • —gradient_accumulation_steps: 1
  • —eval_accumulation_steps: None
  • —torch_empty_cache_steps: None
  • —learning_rate: 5e-05
  • —weight_decay: 0.0
  • —adam_beta1: 0.9
  • —adam_beta2: 0.999
  • —adam_epsilon: 1e-08
  • —max_grad_norm: 1
  • —num_train_epochs: 2
  • —max_steps: -1
  • —lr_scheduler_type: linear
  • —lr_scheduler_kwargs: None
  • —warmup_ratio: None
  • —warmup_steps: 0
  • —log_level: passive
  • —log_level_replica: warning
  • —log_on_each_node: True
  • —logging_nan_inf_filter: True
  • —enable_jit_checkpoint: False
  • —save_on_each_node: False
  • —save_only_model: False
  • —restore_callback_states_from_checkpoint: False
  • —use_cpu: False
  • —seed: 42
  • —data_seed: None
  • —bf16: False
  • —fp16: False
  • —bf16_full_eval: False
  • —fp16_full_eval: False
  • —tf32: None
  • —local_rank: -1
  • —ddp_backend: None
  • —debug: []
  • —dataloader_drop_last: False
  • —dataloader_num_workers: 0
  • —dataloader_prefetch_factor: None
  • —disable_tqdm: False
  • —remove_unused_columns: True
  • —label_names: None
  • —load_best_model_at_end: False
  • —ignore_data_skip: False
  • —fsdp: []
  • —fsdp_config: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
  • —accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': None}
  • —parallelism_config: None
  • —deepspeed: None
  • —label_smoothing_factor: 0.0
  • —optim: adamw_torch
  • —optim_args: None
  • —group_by_length: False
  • —length_column_name: length
  • —project: huggingface
  • —trackio_space_id: trackio
  • —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
  • —push_to_hub: False
  • —resume_from_checkpoint: None
  • —hub_model_id: None
  • —hub_strategy: every_save
  • —hub_private_repo: None
  • —hub_always_push: False
  • —hub_revision: None
  • —gradient_checkpointing: False
  • —gradient_checkpointing_kwargs: None
  • —include_for_metrics: []
  • —eval_do_concat_batches: True
  • —auto_find_batch_size: False
  • —full_determinism: False
  • —ddp_timeout: 1800
  • —torch_compile: False
  • —torch_compile_backend: None
  • —torch_compile_mode: None
  • —include_num_input_tokens_seen: no
  • —neftune_noise_alpha: None
  • —optim_target_modules: None
  • —batch_eval_metrics: False
  • —eval_on_start: False
  • —use_liger_kernel: False
  • —liger_kernel_config: None
  • —eval_use_gather_object: False
  • —average_tokens_across_devices: True
  • —use_cache: False
  • —prompts: None
  • —batch_sampler: batch_sampler
  • —multi_dataset_batch_sampler: round_robin
  • —router_mapping: {}
  • —learning_rate_mapping: {}

</details>

Framework Versions

  • —Python: 3.12.10
  • —Sentence Transformers: 5.2.2
  • —Transformers: 5.0.0
  • —PyTorch: 2.7.0+cu128
  • —Accelerate: 1.12.0
  • —Datasets: 4.5.0
  • —Tokenizers: 0.22.2

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",
}
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}
}

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