metadata

language:
  - en
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:6448
  - loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-base-en-v1.5
widget:
  - source_sentence: How are retail sales data integrated into trading models?
    sentences:
      - >-
        Lagged variables represent historical values of a time series variable
        and are used in forecasting models to capture the impact of past
        observations on future market trends, enhancing the accuracy of
        predictions by incorporating relevant historical information.
      - >-
        Retail sales data reflect consumer spending patterns and overall
        economic activity. Traders analyze this indicator to gauge consumer
        confidence, sectoral performance, and potential market trends related to
        retail-focused stocks.
      - >-
        Regulatory approval for a new drug can have a positive impact on a
        pharmaceutical company's stock price as it opens up new revenue streams
        and market opportunities.
  - source_sentence: What impact does algorithmic trading have on market liquidity?
    sentences:
      - >-
        Volume analysis in stock trading involves studying the number of shares
        or contracts traded in a security or market over a specific period to
        gauge the strength or weakness of a price move.
      - >-
        Social media sentiment analysis can assist in detecting anomalies in
        stock prices by capturing public sentiment and opinions on stocks,
        identifying trends or sudden shifts in sentiment that may precede
        abnormal price movements.
      - >-
        Algorithmic trading can impact market liquidity by increasing trading
        speed, efficiency, and overall trading volume, leading to potential
        liquidity disruptions during certain market conditions.
  - source_sentence: >-
      What considerations should traders take into account when selecting an
      adaptive trading algorithm?
    sentences:
      - >-
        Historical price data helps analysts identify patterns and trends that
        can be used to develop models for predicting future stock prices based
        on past performance.
      - >-
        Traders should consider factors such as performance metrics, risk
        management capabilities, adaptability to changing market conditions,
        data requirements, and the level of transparency and control offered by
        the algorithm.
      - >-
        A stock exchange is a centralized marketplace where securities like
        stocks, bonds, and commodities are bought and sold by investors and
        traders.
  - source_sentence: >-
      How can currency exchange rates and forex markets be integrated into
      trading models alongside macroeconomic indicators?
    sentences:
      - >-
        Moving averages smooth out price data over a specified period, making it
        easier to identify trends and reversals in stock prices.
      - >-
        Currency exchange rates and forex markets are integrated into trading
        models to assess currency risk, international trade impact, and
        cross-border investment opportunities influenced by macroeconomic
        indicators.
      - >-
        Investors use quantitative momentum indicators to identify securities
        that are gaining positive momentum and potentially generating profits by
        buying those assets and selling underperforming assets.
  - source_sentence: >-
      What role does back-testing play in refining event-driven trading
      strategies using historical data and real-time analysis?
    sentences:
      - >-
        Genetic algorithms are well-suited for solving multi-objective
        optimization problems, nonlinear and non-convex optimization problems,
        problems with high-dimensional search spaces, and problems where
        traditional methods may struggle to find optimal solutions.
      - >-
        Risk management techniques such as position sizing, portfolio
        diversification, and stop-loss orders are often used in quantitative
        momentum strategies to manage downside risk and protect against large
        losses.
      - >-
        Back-testing allows traders to evaluate the performance of event-driven
        trading strategies using historical data, identify patterns, optimize
        parameters, and refine strategies for real-time implementation.
datasets:
  - yymYYM/stock_trading_QA
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - cosine_accuracy@3
  - cosine_precision@3
  - cosine_recall@3
  - cosine_ndcg@3
  - cosine_mrr@3
  - cosine_map@3
model-index:
  - name: SentenceTransformer based on BAAI/bge-base-en-v1.5
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: Unknown
          type: unknown
        metrics:
          - type: cosine_accuracy@3
            value: 0.6750348675034867
            name: Cosine Accuracy@3
          - type: cosine_precision@3
            value: 0.22501162250116222
            name: Cosine Precision@3
          - type: cosine_recall@3
            value: 0.6750348675034867
            name: Cosine Recall@3
          - type: cosine_ndcg@3
            value: 0.5838116811117793
            name: Cosine Ndcg@3
          - type: cosine_mrr@3
            value: 0.5523012552301251
            name: Cosine Mrr@3
          - type: cosine_map@3
            value: 0.5523012552301255
            name: Cosine Map@3

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

This is a sentence-transformers model finetuned from BAAI/bge-base-en-v1.5 on the stock_trading_qa 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: BAAI/bge-base-en-v1.5
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- stock_trading_qa
Language: en

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': 768, '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("iamleonie/leonies-test")
# Run inference
sentences = [
    'What role does back-testing play in refining event-driven trading strategies using historical data and real-time analysis?',
    'Back-testing allows traders to evaluate the performance of event-driven trading strategies using historical data, identify patterns, optimize parameters, and refine strategies for real-time implementation.',
    'Risk management techniques such as position sizing, portfolio diversification, and stop-loss orders are often used in quantitative momentum strategies to manage downside risk and protect against large losses.',
]
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

Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@3	0.675
cosine_precision@3	0.225
cosine_recall@3	0.675
cosine_ndcg@3	0.5838
cosine_mrr@3	0.5523
cosine_map@3	0.5523

Training Details

Training Dataset

stock_trading_qa

Dataset: stock_trading_qa at 35dab2e
Size: 6,448 training samples
Columns: anchor and context
Approximate statistics based on the first 1000 samples:
anchor context
type string string
details
min: 7 tokens
mean: 15.83 tokens
max: 39 tokens

min: 17 tokens
mean: 34.67 tokens
max: 59 tokens

	anchor	context
type	string	string
details	min: 7 tokens mean: 15.83 tokens max: 39 tokens	min: 17 tokens mean: 34.67 tokens max: 59 tokens

Samples:

anchor	context
`How should I approach investing in a volatile stock market?`	`Diversify your portfolio, invest in stable companies, consider dollar-cost averaging, and stay informed about market trends to make informed trading decisions.`
`What is the role of cross-validation in assessing the performance of time series forecasting models for stock market trends?`	`Cross-validation helps evaluate the generalization ability of forecasting models by partitioning historical data into training and validation sets, ensuring that the model's performance is robust and reliable for future predictions.`
`What role does correlation play in statistical arbitrage and pair trading?`	`Correlation measures the relationship between asset prices and helps traders identify pairs that exhibit a stable price relationship suitable for pair trading.`

Loss: MultipleNegativesRankingLoss with these parameters:

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

Evaluation Dataset

stock_trading_qa

Dataset: stock_trading_qa at 35dab2e
Size: 717 evaluation samples
Columns: anchor and context
Approximate statistics based on the first 717 samples:
anchor context
type string string
details
min: 7 tokens
mean: 15.96 tokens
max: 30 tokens

min: 17 tokens
mean: 35.03 tokens
max: 62 tokens

	anchor	context
type	string	string
details	min: 7 tokens mean: 15.96 tokens max: 30 tokens	min: 17 tokens mean: 35.03 tokens max: 62 tokens

Samples:

anchor	context
`How can anomaly detection in stock prices be used to identify market inefficiencies and opportunities for arbitrage?`	`Anomaly detection can help identify market inefficiencies by spotting mispricings and opportunities for arbitrage, where traders can exploit price differentials to make profits by trading on anomalies.`
`How do traders interpret the Head and Shoulders pattern as a trading signal?`	`The Head and Shoulders pattern is a reversal pattern with three peaks, where the middle peak (head) is higher than the other two (shoulders), signaling a potential trend reversal and offering a bearish trading signal.`
`How do traders use Fibonacci levels as trading signals?`	`Fibonacci levels are used as trading signals to identify potential support and resistance levels, trend reversals, and price targets in financial markets.`

Loss: MultipleNegativesRankingLoss 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
gradient_accumulation_steps: 16
learning_rate: 2e-05
num_train_epochs: 4
lr_scheduler_type: cosine
warmup_ratio: 0.1
fp16: True
optim: adamw_8bit
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

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: 16
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: 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: False
fp16: True
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_8bit
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
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	cosine_ndcg@3
-1	-1	-	-	0.4451
0.3970	10	5.7817	0.0765	0.5278
0.7940	20	1.295	0.0251	0.5608
1.1588	30	0.6208	0.0209	0.5771
1.5558	40	0.5701	0.0183	0.5789
1.9529	50	0.4546	0.0171	0.5882
2.3176	60	0.2861	0.0160	0.5839
2.7146	70	0.3315	0.0154	0.5818
3.0794	80	0.3179	0.0152	0.5852
3.4764	90	0.367	0.0150	0.5843
3.8734	100	0.354	0.0150	0.5838

Framework Versions

Python: 3.11.12
Sentence Transformers: 4.1.0
Transformers: 4.52.4
PyTorch: 2.6.0+cu124
Accelerate: 1.7.0
Datasets: 3.6.0
Tokenizers: 0.21.1

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}
}