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--- |
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language: |
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- en |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:6448 |
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- loss:MultipleNegativesRankingLoss |
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base_model: BAAI/bge-base-en-v1.5 |
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widget: |
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- source_sentence: How are retail sales data integrated into trading models? |
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sentences: |
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- Lagged variables represent historical values of a time series variable and are |
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used in forecasting models to capture the impact of past observations on future |
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market trends, enhancing the accuracy of predictions by incorporating relevant |
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historical information. |
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- Retail sales data reflect consumer spending patterns and overall economic activity. |
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Traders analyze this indicator to gauge consumer confidence, sectoral performance, |
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and potential market trends related to retail-focused stocks. |
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- Regulatory approval for a new drug can have a positive impact on a pharmaceutical |
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company's stock price as it opens up new revenue streams and market opportunities. |
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- source_sentence: What impact does algorithmic trading have on market liquidity? |
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sentences: |
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- Volume analysis in stock trading involves studying the number of shares or contracts |
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traded in a security or market over a specific period to gauge the strength or |
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weakness of a price move. |
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- Social media sentiment analysis can assist in detecting anomalies in stock prices |
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by capturing public sentiment and opinions on stocks, identifying trends or sudden |
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shifts in sentiment that may precede abnormal price movements. |
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- Algorithmic trading can impact market liquidity by increasing trading speed, efficiency, |
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and overall trading volume, leading to potential liquidity disruptions during |
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certain market conditions. |
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- source_sentence: What considerations should traders take into account when selecting |
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an adaptive trading algorithm? |
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sentences: |
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- Historical price data helps analysts identify patterns and trends that can be |
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used to develop models for predicting future stock prices based on past performance. |
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- Traders should consider factors such as performance metrics, risk management capabilities, |
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adaptability to changing market conditions, data requirements, and the level of |
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transparency and control offered by the algorithm. |
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- A stock exchange is a centralized marketplace where securities like stocks, bonds, |
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and commodities are bought and sold by investors and traders. |
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- source_sentence: How can currency exchange rates and forex markets be integrated |
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into trading models alongside macroeconomic indicators? |
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sentences: |
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- Moving averages smooth out price data over a specified period, making it easier |
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to identify trends and reversals in stock prices. |
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- Currency exchange rates and forex markets are integrated into trading models to |
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assess currency risk, international trade impact, and cross-border investment |
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opportunities influenced by macroeconomic indicators. |
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- Investors use quantitative momentum indicators to identify securities that are |
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gaining positive momentum and potentially generating profits by buying those assets |
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and selling underperforming assets. |
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- source_sentence: What role does back-testing play in refining event-driven trading |
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strategies using historical data and real-time analysis? |
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sentences: |
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- Genetic algorithms are well-suited for solving multi-objective optimization problems, |
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nonlinear and non-convex optimization problems, problems with high-dimensional |
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search spaces, and problems where traditional methods may struggle to find optimal |
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solutions. |
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- Risk management techniques such as position sizing, portfolio diversification, |
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and stop-loss orders are often used in quantitative momentum strategies to manage |
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downside risk and protect against large losses. |
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- Back-testing allows traders to evaluate the performance of event-driven trading |
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strategies using historical data, identify patterns, optimize parameters, and |
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refine strategies for real-time implementation. |
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datasets: |
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- yymYYM/stock_trading_QA |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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metrics: |
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- cosine_accuracy@3 |
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- cosine_precision@3 |
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- cosine_recall@3 |
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- cosine_ndcg@3 |
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- cosine_mrr@3 |
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- cosine_map@3 |
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model-index: |
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- name: SentenceTransformer based on BAAI/bge-base-en-v1.5 |
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results: |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: Unknown |
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type: unknown |
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metrics: |
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- type: cosine_accuracy@3 |
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value: 0.6750348675034867 |
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name: Cosine Accuracy@3 |
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- type: cosine_precision@3 |
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value: 0.22501162250116222 |
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name: Cosine Precision@3 |
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- type: cosine_recall@3 |
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value: 0.6750348675034867 |
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name: Cosine Recall@3 |
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- type: cosine_ndcg@3 |
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value: 0.5838116811117793 |
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name: Cosine Ndcg@3 |
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- type: cosine_mrr@3 |
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value: 0.5523012552301251 |
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name: Cosine Mrr@3 |
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- type: cosine_map@3 |
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value: 0.5523012552301255 |
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name: Cosine Map@3 |
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--- |
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# SentenceTransformer based on BAAI/bge-base-en-v1.5 |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) on the [stock_trading_qa](https://huggingface.co/datasets/yymYYM/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. |
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 768 dimensions |
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- **Similarity Function:** Cosine Similarity |
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- **Training Dataset:** |
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- [stock_trading_qa](https://huggingface.co/datasets/yymYYM/stock_trading_QA) |
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- **Language:** en |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel |
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(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}) |
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(2): Normalize() |
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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("iamleonie/leonies-test") |
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# Run inference |
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sentences = [ |
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'What role does back-testing play in refining event-driven trading strategies using historical data and real-time analysis?', |
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'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.', |
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'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.', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 768] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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## Evaluation |
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### Metrics |
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#### Information Retrieval |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
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| Metric | Value | |
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|:-------------------|:-----------| |
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| cosine_accuracy@3 | 0.675 | |
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| cosine_precision@3 | 0.225 | |
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| cosine_recall@3 | 0.675 | |
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| **cosine_ndcg@3** | **0.5838** | |
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| cosine_mrr@3 | 0.5523 | |
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| cosine_map@3 | 0.5523 | |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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### Training Dataset |
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#### stock_trading_qa |
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* Dataset: [stock_trading_qa](https://huggingface.co/datasets/yymYYM/stock_trading_QA) at [35dab2e](https://huggingface.co/datasets/yymYYM/stock_trading_QA/tree/35dab2e25b6da10842cfb0f832b715cab3765727) |
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* Size: 6,448 training samples |
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* Columns: <code>anchor</code> and <code>context</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | anchor | context | |
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|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 7 tokens</li><li>mean: 15.83 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 34.67 tokens</li><li>max: 59 tokens</li></ul> | |
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* Samples: |
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| anchor | context | |
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|:------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| <code>How should I approach investing in a volatile stock market?</code> | <code>Diversify your portfolio, invest in stable companies, consider dollar-cost averaging, and stay informed about market trends to make informed trading decisions.</code> | |
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| <code>What is the role of cross-validation in assessing the performance of time series forecasting models for stock market trends?</code> | <code>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.</code> | |
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| <code>What role does correlation play in statistical arbitrage and pair trading?</code> | <code>Correlation measures the relationship between asset prices and helps traders identify pairs that exhibit a stable price relationship suitable for pair trading.</code> | |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "cos_sim" |
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} |
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``` |
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### Evaluation Dataset |
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#### stock_trading_qa |
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* Dataset: [stock_trading_qa](https://huggingface.co/datasets/yymYYM/stock_trading_QA) at [35dab2e](https://huggingface.co/datasets/yymYYM/stock_trading_QA/tree/35dab2e25b6da10842cfb0f832b715cab3765727) |
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* Size: 717 evaluation samples |
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* Columns: <code>anchor</code> and <code>context</code> |
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* Approximate statistics based on the first 717 samples: |
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| | anchor | context | |
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|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 7 tokens</li><li>mean: 15.96 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 35.03 tokens</li><li>max: 62 tokens</li></ul> | |
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* Samples: |
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| anchor | context | |
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|:----------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| <code>How can anomaly detection in stock prices be used to identify market inefficiencies and opportunities for arbitrage?</code> | <code>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.</code> | |
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| <code>How do traders interpret the Head and Shoulders pattern as a trading signal?</code> | <code>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.</code> | |
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| <code>How do traders use Fibonacci levels as trading signals?</code> | <code>Fibonacci levels are used as trading signals to identify potential support and resistance levels, trend reversals, and price targets in financial markets.</code> | |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "cos_sim" |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `eval_strategy`: steps |
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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `gradient_accumulation_steps`: 16 |
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- `learning_rate`: 2e-05 |
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- `num_train_epochs`: 4 |
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- `lr_scheduler_type`: cosine |
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- `warmup_ratio`: 0.1 |
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- `fp16`: True |
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- `optim`: adamw_8bit |
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- `batch_sampler`: no_duplicates |
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: steps |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 16 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 2e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 4 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: cosine |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.1 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: True |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: False |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_8bit |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: None |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `include_for_metrics`: [] |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `eval_on_start`: False |
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- `use_liger_kernel`: False |
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- `eval_use_gather_object`: False |
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- `average_tokens_across_devices`: False |
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|
- `prompts`: None |
|
|
- `batch_sampler`: no_duplicates |
|
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
|
|
</details> |
|
|
|
|
|
### 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 |
|
|
```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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|
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