Finance2_embedding_small_en-V1.5

SentenceTransformer based on BAAI/bge-small-en-v1.5

This is a sentence-transformers model finetuned from BAAI/bge-small-en-v1.5 on the baconnier/finance_dataset_small_private dataset. It maps sentences & paragraphs to a 384-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: BAAI/bge-small-en-v1.5 <!-- at revision 5c38ec7c405ec4b44b94cc5a9bb96e735b38267a -->
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 384 tokens
• Similarity Function: Cosine Similarity
• Training Dataset:
  • baconnier/finance_dataset_small_private

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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': True}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, '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})
  (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("baconnier/Finance2_embedding_small_en-V1.5")
# Run inference
sentences = [
    'What is industrial production, and how is it measured by the Federal Reserve Board?',
    'Industrial production is a statistic determined by the Federal Reserve Board that measures the total output of all US factories and mines on a monthly basis. The Fed collects data from various government agencies and trade associations to calculate the industrial production index, which serves as an important economic indicator, providing insight into the health of the manufacturing and mining sectors.\nIndustrial production is a monthly statistic calculated by the Federal Reserve Board, measuring the total output of US factories and mines using data from government agencies and trade associations, serving as a key economic indicator for the manufacturing and mining sectors.',
    'Industrial production is a statistic that measures the output of factories and mines in the US. It is released by the Federal Reserve Board every quarter.\nIndustrial production measures factory and mine output, released quarterly by the Fed.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

<!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* -->

Evaluation

Metrics

Triplet

• Dataset: Finance_Embedding_Metric
• Evaluated with <code>TripletEvaluator</code>

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Training Details

Training Dataset

baconnier/finance_dataset_small_private

• Dataset: baconnier/finance_dataset_small_private at d7e6492
• Size: 15,525 training samples
• Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	<ul><li>min: 11 tokens</li><li>mean: 76.86 tokens</li><li>max: 304 tokens</li></ul>	<ul><li>min: 10 tokens</li><li>mean: 79.23 tokens</li><li>max: 299 tokens</li></ul>	<ul><li>min: 14 tokens</li><li>mean: 60.36 tokens</li><li>max: 155 tokens</li></ul>

• Samples:

  anchor	positive	negative
  <code>What is the key difference between a whole loan and a participation loan in terms of investment ownership?</code>	<code>The context clearly states that a whole loan is a type of investment where an investor purchases the entire mortgage loan from the original lender, becoming the sole owner. This is in contrast to a participation loan, where multiple investors share ownership of a single loan. Therefore, the key difference between a whole loan and a participation loan is that a whole loan is owned entirely by a single investor, while a participation loan involves shared ownership among multiple investors.<br>In a whole loan, a single investor owns the entire mortgage loan, while in a participation loan, multiple investors share ownership of the loan.</code>	<code>A whole loan is where multiple investors share ownership of a loan, while a participation loan is where an investor purchases the entire loan. Since the context states that a whole loan is where an investor purchases the entire mortgage loan and becomes the sole owner, this answer is incorrect.<br>A whole loan involves multiple investors, while a participation loan is owned by a single investor.</code>
  <code>The role of an executor is to manage and distribute the assets of a deceased person's estate in accordance with their will. This includes tasks such as settling debts, filing tax returns, and ensuring that the assets are distributed to the beneficiaries as specified in the will. The executor is appointed by the court to carry out these duties. In the given context, Michael Johnson was nominated by John Smith in his will and appointed by the court as the executor of John's estate, which was valued at $5 million. Michael's responsibilities include dividing the estate equally among John's three children, donating $500,000 to the local animal shelter as per John's instructions, settling the $200,000 mortgage and $50,000 credit card debt, and filing John's final income tax return and paying any outstanding taxes.<br>An executor, appointed by the court, manages and distributes a deceased person's assets according to their will, settling debts, filing taxes, and ensuring the will is followed.</code>	<code>What is the role of an executor in managing a deceased person's estate?</code>	<code>An executor is someone who manages a deceased person's estate. They are responsible for distributing the assets according to the will. In this case, John Smith passed away and nominated Michael Johnson as the executor.<br>The executor is responsible for distributing the assets of a deceased person's estate according to their will.</code>
  <code>What is a ticker tape, and how does it help investors?</code>	<code>A ticker tape is a computerized device that relays stock symbols, latest prices, and trading volumes to investors worldwide in real-time. It helps investors by providing up-to-the-second information about the stocks they are monitoring or interested in, enabling them to make quick and informed trading decisions based on the most current market data available.<br>A ticker tape is a real-time digital stock data display that empowers investors to make timely, data-driven trading decisions by providing the latest stock symbols, prices, and volumes.</code>	<code>A ticker tape is a device that shows stock information. It helps investors by providing some data about stocks.<br>A ticker tape provides stock data to investors.</code>

• Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

Evaluation Dataset

baconnier/finance_dataset_small_private

• Dataset: baconnier/finance_dataset_small_private at d7e6492
• Size: 862 evaluation samples
• Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	<ul><li>min: 10 tokens</li><li>mean: 78.51 tokens</li><li>max: 286 tokens</li></ul>	<ul><li>min: 12 tokens</li><li>mean: 76.02 tokens</li><li>max: 304 tokens</li></ul>	<ul><li>min: 20 tokens</li><li>mean: 59.8 tokens</li><li>max: 271 tokens</li></ul>

• Samples:

  anchor	positive	negative
  <code>What is the underwriter's discount in the given IPO scenario, and how does it relate to the gross spread?</code>	<code>The underwriter's discount is the difference between the price the underwriter pays for the shares and the price at which they sell them to the public. In this case, the underwriter buys the shares at a 7% discount from the IPO price of $20 per share. The underwriter's discount is also known as the gross spread, as it represents the gross profit earned by the underwriter.<br>The underwriter's discount is 7%, which is equivalent to $1.40 per share. This is also known as the gross spread, representing the underwriter's gross profit.</code>	<code>The underwriter's discount is the difference between the price the underwriter pays for the shares and the price at which they sell them to the public. In this case, the underwriter buys the shares at a 7% discount, but the gross spread is not mentioned.<br>The underwriter's discount is 7%, but the gross spread is unknown.</code>
  <code>What is the primary function of the equity market, and how does it relate to the stock market?</code>	<code>The equity market, synonymous with the stock market, serves as a platform for companies to issue ownership shares to raise capital for growth and expansion. Simultaneously, it allows investors to buy these shares, becoming part-owners of the companies and potentially earning returns through stock price appreciation and dividends. The equity market plays a vital role in the financial system by efficiently allocating capital to businesses and providing investment opportunities to individuals and institutions.<br>The equity market, or stock market, primarily functions as a mechanism for companies to raise capital by issuing ownership shares, while providing investors with opportunities to invest in these companies and earn returns, thus facilitating efficient capital allocation in the financial system.</code>	<code>The equity market is where ownership shares of companies are bought and sold. It allows companies to raise money by selling stocks. The stock market is the same as the equity market.<br>The equity market and the stock market are the same thing, where stocks are traded.</code>
  <code>A selling syndicate is a group of investment banks that work together to underwrite and distribute a new security issue, such as stocks or bonds, to investors. The syndicate is typically led by one or more lead underwriters, who coordinate the distribution of the securities and set the offering price. In the case of XYZ Corporation, the selling syndicate is led by ABC Investment Bank and consists of 5 investment banks in total. The syndicate has agreed to purchase 10 million new shares from XYZ Corporation at a fixed price of $50 per share, which they will then sell to investors at a higher price of $55 per share. This process allows XYZ Corporation to raise capital by issuing new shares, while the selling syndicate earns a commission on the sale of the shares. The syndicate's role is to facilitate the distribution of the new shares to a wider pool of investors, helping to ensure the success of the offering.<br>A selling syndicate is a group of investment banks that jointly underwrite and distribute a new security issue to investors. In XYZ Corporation's case, the syndicate will purchase shares from the company at a fixed price and resell them to investors at a higher price, earning a commission and facilitating the successful distribution of the new shares.</code>	<code>What is a selling syndicate, and how does it function in the context of XYZ Corporation's new share issue?</code>	<code>A selling syndicate is a group of investment banks that work together to sell new shares of a company. In this case, XYZ Corporation has hired 5 investment banks to sell their new shares. The syndicate buys the shares from XYZ Corporation at a fixed price and then sells them to investors at a higher price.<br>A selling syndicate is a group of investment banks that jointly underwrite and distribute new shares of a company to investors, buying the shares at a fixed price and selling them at a higher price.</code>

• Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• num_train_epochs: 1
• warmup_ratio: 0.1
• bf16: True
• batch_sampler: no_duplicates

All Hyperparameters

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

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 1
• eval_accumulation_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: 1.0
• num_train_epochs: 1
• max_steps: -1
• lr_scheduler_type: linear
• 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: False
• 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
• 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

</details>

Training Logs

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 3.0.1
• Transformers: 4.41.2
• PyTorch: 2.3.0+cu121
• Accelerate: 0.31.0
• Datasets: 2.19.2
• 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",
}

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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