| --- |
| base_model: BAAI/bge-base-en-v1.5 |
| datasets: [] |
| language: |
| - en |
| library_name: sentence-transformers |
| license: apache-2.0 |
| metrics: |
| - cosine_accuracy@1 |
| - cosine_accuracy@3 |
| - cosine_accuracy@5 |
| - cosine_accuracy@10 |
| - cosine_precision@1 |
| - cosine_precision@3 |
| - cosine_precision@5 |
| - cosine_precision@10 |
| - cosine_recall@1 |
| - cosine_recall@3 |
| - cosine_recall@5 |
| - cosine_recall@10 |
| - cosine_ndcg@10 |
| - cosine_mrr@10 |
| - cosine_map@100 |
| pipeline_tag: sentence-similarity |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - generated_from_trainer |
| - dataset_size:6300 |
| - loss:MatryoshkaLoss |
| - loss:MultipleNegativesRankingLoss |
| widget: |
| - source_sentence: As of December 31, 2023, Hilton franchised 6,679 hotels and resorts, |
| with 914,974 rooms. |
| sentences: |
| - What does Google's new model 'Gemini' aim to achieve? |
| - What is the total number of rooms in Hilton's franchised hotels as of December |
| 31, 2023? |
| - How much is the Company agreed to pay under the opioid settlement to resolve all |
| lawsuits and future claims by government entities nationwide? |
| - source_sentence: Under the Biologics Price Competition and Innovation Act, innovator |
| biologics are granted a regulatory exclusivity period of 12 years. |
| sentences: |
| - What are the primary goals of the asset allocation strategy for USRIP's plan, |
| and what standards must investment managers follow? |
| - How long is the regulatory exclusivity period for innovator biologics under the |
| Biologics Price Competition and Innovation Act? |
| - By what percentage did the office loans increase in exposure during 2023? |
| - source_sentence: Amounts recorded in a business combination may change during the |
| measurement period, which is a period not to exceed one year from the date of |
| acquisition, as additional information about conditions that existed at the acquisition |
| date becomes available. |
| sentences: |
| - What is considered during the measurement period in a business combination? |
| - What was the primary reason for the increase in other costs of $15.3 million reported? |
| - How is the stock-based compensation expense determined for service-based and performance |
| or market condition awards at Hewlett Packard Enterprise? |
| - source_sentence: 'The Be Human pillar of our Impact Agenda sets out our focus areas |
| with respect to human capital, including: •Inclusion, Diversity, Equity, and Action |
| (“IDEA”); •Employee empowerment; and •Fair labor practices and the well-being |
| of the people who make our products.' |
| sentences: |
| - How did Hilton Worldwide Holdings Inc.'s accumulated deficit change from December |
| 31, 2022, to December 31, 2023? |
| - What primarily caused the decrease in the Company's effective income tax rate |
| in 2023? |
| - What is the objective of the Be Human pillar in the company's Impact Agenda? |
| - source_sentence: Our revenue consists of service fees, net of incentives and refunds, |
| charged to our customers. For stays, service fees, which are charged to customers |
| as a percentage of the value of the booking, excluding taxes, vary based on factors |
| specific to the booking, such as booking value, the duration of the booking, geography, |
| and Host type. |
| sentences: |
| - What are some factors that affect the percentage of service fees charged to customers? |
| - What is the PCAOB ID number for PricewaterhouseCoopers LLP concerning the firm's |
| financial statements? |
| - What were the net revenues for Global Banking & Markets in 2023? |
| model-index: |
| - name: BGE base Financial Matryoshka |
| results: |
| - task: |
| type: information-retrieval |
| name: Information Retrieval |
| dataset: |
| name: dim 768 |
| type: dim_768 |
| metrics: |
| - type: cosine_accuracy@1 |
| value: 0.6957142857142857 |
| name: Cosine Accuracy@1 |
| - type: cosine_accuracy@3 |
| value: 0.8 |
| name: Cosine Accuracy@3 |
| - type: cosine_accuracy@5 |
| value: 0.8485714285714285 |
| name: Cosine Accuracy@5 |
| - type: cosine_accuracy@10 |
| value: 0.9 |
| name: Cosine Accuracy@10 |
| - type: cosine_precision@1 |
| value: 0.6957142857142857 |
| name: Cosine Precision@1 |
| - type: cosine_precision@3 |
| value: 0.26666666666666666 |
| name: Cosine Precision@3 |
| - type: cosine_precision@5 |
| value: 0.16971428571428568 |
| name: Cosine Precision@5 |
| - type: cosine_precision@10 |
| value: 0.08999999999999998 |
| name: Cosine Precision@10 |
| - type: cosine_recall@1 |
| value: 0.6957142857142857 |
| name: Cosine Recall@1 |
| - type: cosine_recall@3 |
| value: 0.8 |
| name: Cosine Recall@3 |
| - type: cosine_recall@5 |
| value: 0.8485714285714285 |
| name: Cosine Recall@5 |
| - type: cosine_recall@10 |
| value: 0.9 |
| name: Cosine Recall@10 |
| - type: cosine_ndcg@10 |
| value: 0.7935293220413043 |
| name: Cosine Ndcg@10 |
| - type: cosine_mrr@10 |
| value: 0.759959183673469 |
| name: Cosine Mrr@10 |
| - type: cosine_map@100 |
| value: 0.7639893123837201 |
| name: Cosine Map@100 |
| - task: |
| type: information-retrieval |
| name: Information Retrieval |
| dataset: |
| name: dim 512 |
| type: dim_512 |
| metrics: |
| - type: cosine_accuracy@1 |
| value: 0.7057142857142857 |
| name: Cosine Accuracy@1 |
| - type: cosine_accuracy@3 |
| value: 0.8014285714285714 |
| name: Cosine Accuracy@3 |
| - type: cosine_accuracy@5 |
| value: 0.8528571428571429 |
| name: Cosine Accuracy@5 |
| - type: cosine_accuracy@10 |
| value: 0.9028571428571428 |
| name: Cosine Accuracy@10 |
| - type: cosine_precision@1 |
| value: 0.7057142857142857 |
| name: Cosine Precision@1 |
| - type: cosine_precision@3 |
| value: 0.2671428571428571 |
| name: Cosine Precision@3 |
| - type: cosine_precision@5 |
| value: 0.17057142857142854 |
| name: Cosine Precision@5 |
| - type: cosine_precision@10 |
| value: 0.09028571428571427 |
| name: Cosine Precision@10 |
| - type: cosine_recall@1 |
| value: 0.7057142857142857 |
| name: Cosine Recall@1 |
| - type: cosine_recall@3 |
| value: 0.8014285714285714 |
| name: Cosine Recall@3 |
| - type: cosine_recall@5 |
| value: 0.8528571428571429 |
| name: Cosine Recall@5 |
| - type: cosine_recall@10 |
| value: 0.9028571428571428 |
| name: Cosine Recall@10 |
| - type: cosine_ndcg@10 |
| value: 0.7983926017556883 |
| name: Cosine Ndcg@10 |
| - type: cosine_mrr@10 |
| value: 0.7656269841269838 |
| name: Cosine Mrr@10 |
| - type: cosine_map@100 |
| value: 0.7693363291720529 |
| name: Cosine Map@100 |
| - task: |
| type: information-retrieval |
| name: Information Retrieval |
| dataset: |
| name: dim 256 |
| type: dim_256 |
| metrics: |
| - type: cosine_accuracy@1 |
| value: 0.6914285714285714 |
| name: Cosine Accuracy@1 |
| - type: cosine_accuracy@3 |
| value: 0.79 |
| name: Cosine Accuracy@3 |
| - type: cosine_accuracy@5 |
| value: 0.8471428571428572 |
| name: Cosine Accuracy@5 |
| - type: cosine_accuracy@10 |
| value: 0.8914285714285715 |
| name: Cosine Accuracy@10 |
| - type: cosine_precision@1 |
| value: 0.6914285714285714 |
| name: Cosine Precision@1 |
| - type: cosine_precision@3 |
| value: 0.2633333333333333 |
| name: Cosine Precision@3 |
| - type: cosine_precision@5 |
| value: 0.16942857142857143 |
| name: Cosine Precision@5 |
| - type: cosine_precision@10 |
| value: 0.08914285714285713 |
| name: Cosine Precision@10 |
| - type: cosine_recall@1 |
| value: 0.6914285714285714 |
| name: Cosine Recall@1 |
| - type: cosine_recall@3 |
| value: 0.79 |
| name: Cosine Recall@3 |
| - type: cosine_recall@5 |
| value: 0.8471428571428572 |
| name: Cosine Recall@5 |
| - type: cosine_recall@10 |
| value: 0.8914285714285715 |
| name: Cosine Recall@10 |
| - type: cosine_ndcg@10 |
| value: 0.7878064776962901 |
| name: Cosine Ndcg@10 |
| - type: cosine_mrr@10 |
| value: 0.7549427437641724 |
| name: Cosine Mrr@10 |
| - type: cosine_map@100 |
| value: 0.7595543581664418 |
| name: Cosine Map@100 |
| - task: |
| type: information-retrieval |
| name: Information Retrieval |
| dataset: |
| name: dim 128 |
| type: dim_128 |
| metrics: |
| - type: cosine_accuracy@1 |
| value: 0.6885714285714286 |
| name: Cosine Accuracy@1 |
| - type: cosine_accuracy@3 |
| value: 0.7928571428571428 |
| name: Cosine Accuracy@3 |
| - type: cosine_accuracy@5 |
| value: 0.8385714285714285 |
| name: Cosine Accuracy@5 |
| - type: cosine_accuracy@10 |
| value: 0.8914285714285715 |
| name: Cosine Accuracy@10 |
| - type: cosine_precision@1 |
| value: 0.6885714285714286 |
| name: Cosine Precision@1 |
| - type: cosine_precision@3 |
| value: 0.2642857142857143 |
| name: Cosine Precision@3 |
| - type: cosine_precision@5 |
| value: 0.1677142857142857 |
| name: Cosine Precision@5 |
| - type: cosine_precision@10 |
| value: 0.08914285714285713 |
| name: Cosine Precision@10 |
| - type: cosine_recall@1 |
| value: 0.6885714285714286 |
| name: Cosine Recall@1 |
| - type: cosine_recall@3 |
| value: 0.7928571428571428 |
| name: Cosine Recall@3 |
| - type: cosine_recall@5 |
| value: 0.8385714285714285 |
| name: Cosine Recall@5 |
| - type: cosine_recall@10 |
| value: 0.8914285714285715 |
| name: Cosine Recall@10 |
| - type: cosine_ndcg@10 |
| value: 0.7855455284623294 |
| name: Cosine Ndcg@10 |
| - type: cosine_mrr@10 |
| value: 0.752206916099773 |
| name: Cosine Mrr@10 |
| - type: cosine_map@100 |
| value: 0.7560619398777708 |
| name: Cosine Map@100 |
| - task: |
| type: information-retrieval |
| name: Information Retrieval |
| dataset: |
| name: dim 64 |
| type: dim_64 |
| metrics: |
| - type: cosine_accuracy@1 |
| value: 0.64 |
| name: Cosine Accuracy@1 |
| - type: cosine_accuracy@3 |
| value: 0.7642857142857142 |
| name: Cosine Accuracy@3 |
| - type: cosine_accuracy@5 |
| value: 0.8114285714285714 |
| name: Cosine Accuracy@5 |
| - type: cosine_accuracy@10 |
| value: 0.8671428571428571 |
| name: Cosine Accuracy@10 |
| - type: cosine_precision@1 |
| value: 0.64 |
| name: Cosine Precision@1 |
| - type: cosine_precision@3 |
| value: 0.25476190476190474 |
| name: Cosine Precision@3 |
| - type: cosine_precision@5 |
| value: 0.16228571428571426 |
| name: Cosine Precision@5 |
| - type: cosine_precision@10 |
| value: 0.0867142857142857 |
| name: Cosine Precision@10 |
| - type: cosine_recall@1 |
| value: 0.64 |
| name: Cosine Recall@1 |
| - type: cosine_recall@3 |
| value: 0.7642857142857142 |
| name: Cosine Recall@3 |
| - type: cosine_recall@5 |
| value: 0.8114285714285714 |
| name: Cosine Recall@5 |
| - type: cosine_recall@10 |
| value: 0.8671428571428571 |
| name: Cosine Recall@10 |
| - type: cosine_ndcg@10 |
| value: 0.7491977147487785 |
| name: Cosine Ndcg@10 |
| - type: cosine_mrr@10 |
| value: 0.711975623582766 |
| name: Cosine Mrr@10 |
| - type: cosine_map@100 |
| value: 0.7167882776968978 |
| name: Cosine Map@100 |
| --- |
| |
| # BGE base Financial Matryoshka |
|
|
| 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). 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](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a --> |
| - **Maximum Sequence Length:** 512 tokens |
| - **Output Dimensionality:** 768 tokens |
| - **Similarity Function:** Cosine Similarity |
| <!-- - **Training Dataset:** Unknown --> |
| - **Language:** en |
| - **License:** apache-2.0 |
|
|
| ### Model Sources |
|
|
| - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
| - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
| - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
|
|
| ### 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: |
|
|
| ```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("bhlim/bge-base-financial-matryoshka") |
| # Run inference |
| sentences = [ |
| 'Our revenue consists of service fees, net of incentives and refunds, charged to our customers. For stays, service fees, which are charged to customers as a percentage of the value of the booking, excluding taxes, vary based on factors specific to the booking, such as booking value, the duration of the booking, geography, and Host type.', |
| 'What are some factors that affect the percentage of service fees charged to customers?', |
| "What is the PCAOB ID number for PricewaterhouseCoopers LLP concerning the firm's financial statements?", |
| ] |
| 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] |
| ``` |
|
|
| <!-- |
| ### 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 |
|
|
| #### Information Retrieval |
| * Dataset: `dim_768` |
| * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
| | Metric | Value | |
| |:--------------------|:----------| |
| | cosine_accuracy@1 | 0.6957 | |
| | cosine_accuracy@3 | 0.8 | |
| | cosine_accuracy@5 | 0.8486 | |
| | cosine_accuracy@10 | 0.9 | |
| | cosine_precision@1 | 0.6957 | |
| | cosine_precision@3 | 0.2667 | |
| | cosine_precision@5 | 0.1697 | |
| | cosine_precision@10 | 0.09 | |
| | cosine_recall@1 | 0.6957 | |
| | cosine_recall@3 | 0.8 | |
| | cosine_recall@5 | 0.8486 | |
| | cosine_recall@10 | 0.9 | |
| | cosine_ndcg@10 | 0.7935 | |
| | cosine_mrr@10 | 0.76 | |
| | **cosine_map@100** | **0.764** | |
| |
| #### Information Retrieval |
| * Dataset: `dim_512` |
| * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
| |
| | Metric | Value | |
| |:--------------------|:-----------| |
| | cosine_accuracy@1 | 0.7057 | |
| | cosine_accuracy@3 | 0.8014 | |
| | cosine_accuracy@5 | 0.8529 | |
| | cosine_accuracy@10 | 0.9029 | |
| | cosine_precision@1 | 0.7057 | |
| | cosine_precision@3 | 0.2671 | |
| | cosine_precision@5 | 0.1706 | |
| | cosine_precision@10 | 0.0903 | |
| | cosine_recall@1 | 0.7057 | |
| | cosine_recall@3 | 0.8014 | |
| | cosine_recall@5 | 0.8529 | |
| | cosine_recall@10 | 0.9029 | |
| | cosine_ndcg@10 | 0.7984 | |
| | cosine_mrr@10 | 0.7656 | |
| | **cosine_map@100** | **0.7693** | |
| |
| #### Information Retrieval |
| * Dataset: `dim_256` |
| * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
| | Metric | Value | |
| |:--------------------|:-----------| |
| | cosine_accuracy@1 | 0.6914 | |
| | cosine_accuracy@3 | 0.79 | |
| | cosine_accuracy@5 | 0.8471 | |
| | cosine_accuracy@10 | 0.8914 | |
| | cosine_precision@1 | 0.6914 | |
| | cosine_precision@3 | 0.2633 | |
| | cosine_precision@5 | 0.1694 | |
| | cosine_precision@10 | 0.0891 | |
| | cosine_recall@1 | 0.6914 | |
| | cosine_recall@3 | 0.79 | |
| | cosine_recall@5 | 0.8471 | |
| | cosine_recall@10 | 0.8914 | |
| | cosine_ndcg@10 | 0.7878 | |
| | cosine_mrr@10 | 0.7549 | |
| | **cosine_map@100** | **0.7596** | |
| |
| #### Information Retrieval |
| * Dataset: `dim_128` |
| * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
| |
| | Metric | Value | |
| |:--------------------|:-----------| |
| | cosine_accuracy@1 | 0.6886 | |
| | cosine_accuracy@3 | 0.7929 | |
| | cosine_accuracy@5 | 0.8386 | |
| | cosine_accuracy@10 | 0.8914 | |
| | cosine_precision@1 | 0.6886 | |
| | cosine_precision@3 | 0.2643 | |
| | cosine_precision@5 | 0.1677 | |
| | cosine_precision@10 | 0.0891 | |
| | cosine_recall@1 | 0.6886 | |
| | cosine_recall@3 | 0.7929 | |
| | cosine_recall@5 | 0.8386 | |
| | cosine_recall@10 | 0.8914 | |
| | cosine_ndcg@10 | 0.7855 | |
| | cosine_mrr@10 | 0.7522 | |
| | **cosine_map@100** | **0.7561** | |
| |
| #### Information Retrieval |
| * Dataset: `dim_64` |
| * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
| | Metric | Value | |
| |:--------------------|:-----------| |
| | cosine_accuracy@1 | 0.64 | |
| | cosine_accuracy@3 | 0.7643 | |
| | cosine_accuracy@5 | 0.8114 | |
| | cosine_accuracy@10 | 0.8671 | |
| | cosine_precision@1 | 0.64 | |
| | cosine_precision@3 | 0.2548 | |
| | cosine_precision@5 | 0.1623 | |
| | cosine_precision@10 | 0.0867 | |
| | cosine_recall@1 | 0.64 | |
| | cosine_recall@3 | 0.7643 | |
| | cosine_recall@5 | 0.8114 | |
| | cosine_recall@10 | 0.8671 | |
| | cosine_ndcg@10 | 0.7492 | |
| | cosine_mrr@10 | 0.712 | |
| | **cosine_map@100** | **0.7168** | |
| |
| <!-- |
| ## Bias, Risks and Limitations |
| |
| *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| --> |
| |
| <!-- |
| ### Recommendations |
| |
| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| --> |
| |
| ## Training Details |
| |
| ### Training Dataset |
| |
| #### Unnamed Dataset |
| |
| |
| * Size: 6,300 training samples |
| * Columns: <code>positive</code> and <code>anchor</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | positive | anchor | |
| |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
| | type | string | string | |
| | details | <ul><li>min: 8 tokens</li><li>mean: 46.18 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 20.64 tokens</li><li>max: 42 tokens</li></ul> | |
| * Samples: |
| | positive | anchor | |
| |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------| |
| | <code>Within the contiguous U.S., FedEx Freight offers FedEx Freight Priority, when speed is critical to meet a customer’s supply chain needs.</code> | <code>How does FedEx Freight accommodate rapid delivery needs?</code> | |
| | <code>For purposes of our goodwill impairment evaluation, the reporting units are Family Dollar, Dollar Tree and Dollar Tree Canada.</code> | <code>What reporting units are used for the goodwill impairment evaluation?</code> | |
| | <code>In 2024, AT&T Inc. expects a long-term rate of return of 7.75% on pension plan assets, reflecting an increase of 0.25%. This adjustment in expected returns is based on economic forecasts and changes in the asset mix.</code> | <code>What will AT&T Inc.'s expected long-term rate of return be on pension plan assets in 2024?</code> | |
| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
| ```json |
| { |
| "loss": "MultipleNegativesRankingLoss", |
| "matryoshka_dims": [ |
| 768, |
| 512, |
| 256, |
| 128, |
| 64 |
| ], |
| "matryoshka_weights": [ |
| 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 |
| <details><summary>Click to expand</summary> |
| |
| - `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 |
| |
| </details> |
| |
| ### Training Logs |
| | Epoch | Step | Training Loss | 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 | |
| |:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| |
| | 0.8122 | 10 | 1.5825 | - | - | - | - | - | |
| | 0.9746 | 12 | - | 0.7349 | 0.7502 | 0.7566 | 0.6910 | 0.7566 | |
| | 1.6244 | 20 | 0.6595 | - | - | - | - | - | |
| | 1.9492 | 24 | - | 0.7508 | 0.7583 | 0.7648 | 0.7142 | 0.7615 | |
| | 2.4365 | 30 | 0.4717 | - | - | - | - | - | |
| | **2.9239** | **36** | **-** | **0.7562** | **0.7616** | **0.7692** | **0.7178** | **0.7622** | |
| | 3.2487 | 40 | 0.4059 | - | - | - | - | - | |
| | 3.8985 | 48 | - | 0.7561 | 0.7596 | 0.7693 | 0.7168 | 0.7640 | |
|
|
| * The bold row denotes the saved checkpoint. |
|
|
| ### Framework Versions |
| - Python: 3.10.12 |
| - Sentence Transformers: 3.0.1 |
| - Transformers: 4.41.2 |
| - PyTorch: 2.3.1+cu121 |
| - Accelerate: 0.32.1 |
| - Datasets: 2.19.1 |
| - Tokenizers: 0.19.1 |
|
|
| ## 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", |
| } |
| ``` |
|
|
| #### MatryoshkaLoss |
| ```bibtex |
| @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 |
| ```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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