| | --- |
| | 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: A number of factors may impact ESKD growth rates, including mortality |
| | rates for dialysis patients or CKD patients, the aging of the U.S. population, |
| | transplant rates, incidence rates for diseases that cause kidney failure such |
| | as diabetes and hypertension, growth rates of minority populations with higher |
| | than average incidence rates of ESKD. |
| | sentences: |
| | - By how much did the company increase its quarterly cash dividend in February 2023? |
| | - What factors may impact the growth rates of the ESKD patient population? |
| | - What percentage increase did salaries and related costs experience at Delta Air |
| | Lines from 2022 to 2023? |
| | - source_sentence: HIV product sales increased 6% to $18.2 billion in 2023, compared |
| | to 2022. |
| | sentences: |
| | - What were the present values of lease liabilities for operating and finance leases |
| | as of December 31, 2023? |
| | - By what percentage did HIV product sales increase in 2023 compared to the previous |
| | year? |
| | - How is interest income not attributable to the Card Member loan portfolio primarily |
| | represented in financial documents? |
| | - source_sentence: If a violation is found, a broad range of remedies is potentially |
| | available to the Commission and/or CMA, including imposing a fine and/or the prohibition |
| | or restriction of certain business practices. |
| | sentences: |
| | - What are the potential remedies if a violation is found by the European Commission |
| | or the U.K. Competition and Markets Authority in their investigation of automotive |
| | companies? |
| | - By which auditing standards were the consolidated financial statements of Salesforce, |
| | Inc. audited? |
| | - What is the main role of Kroger's Chief Executive Officer in the company? |
| | - source_sentence: The discussion in Hewlett Packard Enterprise's Form 10-K highlights |
| | factors impacting costs and revenues, including easing supply chain constraints, |
| | foreign exchange pressures, inflationary trends, and recent tax developments potentially |
| | affecting their financial outcomes. |
| | sentences: |
| | - Is the outcome of the investigation into Tesla's waste segregation practices currently |
| | determinable? |
| | - How does Hewlett Packard Enterprise justify the exclusion of transformation costs |
| | from its non-GAAP financial measures? |
| | - In the context of Hewlett Packard Enterprise's recent financial discussions, what |
| | factors are expected to impact their operational costs and revenue growth moving |
| | forward? |
| | - source_sentence: Our Records Management and Data Management service revenue growth |
| | is being negatively impacted by declining activity rates as stored records and |
| | tapes are becoming less active and more archival. |
| | sentences: |
| | - How is Iron Mountain addressing the decline in activity rates in their Records |
| | and Data Management services? |
| | - What services do companies that build fiber-based networks provide in the Connectivity |
| | & Platforms markets? |
| | - What business outcomes is HPE focused on accelerating with its technological solutions? |
| | 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: 0.7057142857142857 |
| | name: Cosine Accuracy@1 |
| | - type: cosine_accuracy@3 |
| | value: 0.8457142857142858 |
| | name: Cosine Accuracy@3 |
| | - type: cosine_accuracy@5 |
| | value: 0.8785714285714286 |
| | name: Cosine Accuracy@5 |
| | - type: cosine_accuracy@10 |
| | value: 0.9114285714285715 |
| | name: Cosine Accuracy@10 |
| | - type: cosine_precision@1 |
| | value: 0.7057142857142857 |
| | name: Cosine Precision@1 |
| | - type: cosine_precision@3 |
| | value: 0.2819047619047619 |
| | name: Cosine Precision@3 |
| | - type: cosine_precision@5 |
| | value: 0.17571428571428568 |
| | name: Cosine Precision@5 |
| | - type: cosine_precision@10 |
| | value: 0.09114285714285714 |
| | name: Cosine Precision@10 |
| | - type: cosine_recall@1 |
| | value: 0.7057142857142857 |
| | name: Cosine Recall@1 |
| | - type: cosine_recall@3 |
| | value: 0.8457142857142858 |
| | name: Cosine Recall@3 |
| | - type: cosine_recall@5 |
| | value: 0.8785714285714286 |
| | name: Cosine Recall@5 |
| | - type: cosine_recall@10 |
| | value: 0.9114285714285715 |
| | name: Cosine Recall@10 |
| | - type: cosine_ndcg@10 |
| | value: 0.8125296344519609 |
| | name: Cosine Ndcg@10 |
| | - type: cosine_mrr@10 |
| | value: 0.7804263038548749 |
| | name: Cosine Mrr@10 |
| | - type: cosine_map@100 |
| | value: 0.7839408125709297 |
| | name: Cosine Map@100 |
| | - task: |
| | type: information-retrieval |
| | name: Information Retrieval |
| | dataset: |
| | name: dim 512 |
| | type: dim_512 |
| | metrics: |
| | - type: cosine_accuracy@1 |
| | value: 0.7071428571428572 |
| | name: Cosine Accuracy@1 |
| | - type: cosine_accuracy@3 |
| | value: 0.8428571428571429 |
| | name: Cosine Accuracy@3 |
| | - type: cosine_accuracy@5 |
| | value: 0.8742857142857143 |
| | name: Cosine Accuracy@5 |
| | - type: cosine_accuracy@10 |
| | value: 0.9114285714285715 |
| | name: Cosine Accuracy@10 |
| | - type: cosine_precision@1 |
| | value: 0.7071428571428572 |
| | name: Cosine Precision@1 |
| | - type: cosine_precision@3 |
| | value: 0.28095238095238095 |
| | name: Cosine Precision@3 |
| | - type: cosine_precision@5 |
| | value: 0.17485714285714282 |
| | name: Cosine Precision@5 |
| | - type: cosine_precision@10 |
| | value: 0.09114285714285714 |
| | name: Cosine Precision@10 |
| | - type: cosine_recall@1 |
| | value: 0.7071428571428572 |
| | name: Cosine Recall@1 |
| | - type: cosine_recall@3 |
| | value: 0.8428571428571429 |
| | name: Cosine Recall@3 |
| | - type: cosine_recall@5 |
| | value: 0.8742857142857143 |
| | name: Cosine Recall@5 |
| | - type: cosine_recall@10 |
| | value: 0.9114285714285715 |
| | name: Cosine Recall@10 |
| | - type: cosine_ndcg@10 |
| | value: 0.8126517351231356 |
| | name: Cosine Ndcg@10 |
| | - type: cosine_mrr@10 |
| | value: 0.7807267573696143 |
| | name: Cosine Mrr@10 |
| | - type: cosine_map@100 |
| | value: 0.7841188299664252 |
| | name: Cosine Map@100 |
| | - task: |
| | type: information-retrieval |
| | name: Information Retrieval |
| | dataset: |
| | name: dim 256 |
| | type: dim_256 |
| | metrics: |
| | - type: cosine_accuracy@1 |
| | value: 0.7028571428571428 |
| | name: Cosine Accuracy@1 |
| | - type: cosine_accuracy@3 |
| | value: 0.8357142857142857 |
| | name: Cosine Accuracy@3 |
| | - type: cosine_accuracy@5 |
| | value: 0.8685714285714285 |
| | name: Cosine Accuracy@5 |
| | - type: cosine_accuracy@10 |
| | value: 0.9071428571428571 |
| | name: Cosine Accuracy@10 |
| | - type: cosine_precision@1 |
| | value: 0.7028571428571428 |
| | name: Cosine Precision@1 |
| | - type: cosine_precision@3 |
| | value: 0.2785714285714286 |
| | name: Cosine Precision@3 |
| | - type: cosine_precision@5 |
| | value: 0.1737142857142857 |
| | name: Cosine Precision@5 |
| | - type: cosine_precision@10 |
| | value: 0.09071428571428572 |
| | name: Cosine Precision@10 |
| | - type: cosine_recall@1 |
| | value: 0.7028571428571428 |
| | name: Cosine Recall@1 |
| | - type: cosine_recall@3 |
| | value: 0.8357142857142857 |
| | name: Cosine Recall@3 |
| | - type: cosine_recall@5 |
| | value: 0.8685714285714285 |
| | name: Cosine Recall@5 |
| | - type: cosine_recall@10 |
| | value: 0.9071428571428571 |
| | name: Cosine Recall@10 |
| | - type: cosine_ndcg@10 |
| | value: 0.8086618947757659 |
| | name: Cosine Ndcg@10 |
| | - type: cosine_mrr@10 |
| | value: 0.7768820861678005 |
| | name: Cosine Mrr@10 |
| | - type: cosine_map@100 |
| | value: 0.7806177775944575 |
| | name: Cosine Map@100 |
| | - task: |
| | type: information-retrieval |
| | name: Information Retrieval |
| | dataset: |
| | name: dim 128 |
| | type: dim_128 |
| | metrics: |
| | - type: cosine_accuracy@1 |
| | value: 0.6914285714285714 |
| | name: Cosine Accuracy@1 |
| | - type: cosine_accuracy@3 |
| | value: 0.82 |
| | name: Cosine Accuracy@3 |
| | - type: cosine_accuracy@5 |
| | value: 0.8557142857142858 |
| | name: Cosine Accuracy@5 |
| | - type: cosine_accuracy@10 |
| | value: 0.9014285714285715 |
| | name: Cosine Accuracy@10 |
| | - type: cosine_precision@1 |
| | value: 0.6914285714285714 |
| | name: Cosine Precision@1 |
| | - type: cosine_precision@3 |
| | value: 0.2733333333333334 |
| | name: Cosine Precision@3 |
| | - type: cosine_precision@5 |
| | value: 0.17114285714285712 |
| | name: Cosine Precision@5 |
| | - type: cosine_precision@10 |
| | value: 0.09014285714285714 |
| | name: Cosine Precision@10 |
| | - type: cosine_recall@1 |
| | value: 0.6914285714285714 |
| | name: Cosine Recall@1 |
| | - type: cosine_recall@3 |
| | value: 0.82 |
| | name: Cosine Recall@3 |
| | - type: cosine_recall@5 |
| | value: 0.8557142857142858 |
| | name: Cosine Recall@5 |
| | - type: cosine_recall@10 |
| | value: 0.9014285714285715 |
| | name: Cosine Recall@10 |
| | - type: cosine_ndcg@10 |
| | value: 0.7980982703041672 |
| | name: Cosine Ndcg@10 |
| | - type: cosine_mrr@10 |
| | value: 0.7650045351473919 |
| | name: Cosine Mrr@10 |
| | - type: cosine_map@100 |
| | value: 0.7688564414027702 |
| | name: Cosine Map@100 |
| | - task: |
| | type: information-retrieval |
| | name: Information Retrieval |
| | dataset: |
| | name: dim 64 |
| | type: dim_64 |
| | metrics: |
| | - type: cosine_accuracy@1 |
| | value: 0.6542857142857142 |
| | name: Cosine Accuracy@1 |
| | - type: cosine_accuracy@3 |
| | value: 0.7885714285714286 |
| | name: Cosine Accuracy@3 |
| | - type: cosine_accuracy@5 |
| | value: 0.8328571428571429 |
| | name: Cosine Accuracy@5 |
| | - type: cosine_accuracy@10 |
| | value: 0.8828571428571429 |
| | name: Cosine Accuracy@10 |
| | - type: cosine_precision@1 |
| | value: 0.6542857142857142 |
| | name: Cosine Precision@1 |
| | - type: cosine_precision@3 |
| | value: 0.26285714285714284 |
| | name: Cosine Precision@3 |
| | - type: cosine_precision@5 |
| | value: 0.16657142857142856 |
| | name: Cosine Precision@5 |
| | - type: cosine_precision@10 |
| | value: 0.08828571428571427 |
| | name: Cosine Precision@10 |
| | - type: cosine_recall@1 |
| | value: 0.6542857142857142 |
| | name: Cosine Recall@1 |
| | - type: cosine_recall@3 |
| | value: 0.7885714285714286 |
| | name: Cosine Recall@3 |
| | - type: cosine_recall@5 |
| | value: 0.8328571428571429 |
| | name: Cosine Recall@5 |
| | - type: cosine_recall@10 |
| | value: 0.8828571428571429 |
| | name: Cosine Recall@10 |
| | - type: cosine_ndcg@10 |
| | value: 0.7689665884678363 |
| | name: Cosine Ndcg@10 |
| | - type: cosine_mrr@10 |
| | value: 0.7325351473922898 |
| | name: Cosine Mrr@10 |
| | - type: cosine_map@100 |
| | value: 0.7369423610264151 |
| | 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("NickyNicky/bge-base-financial-matryoshka") |
| | # Run inference |
| | sentences = [ |
| | 'Our Records Management and Data Management service revenue growth is being negatively impacted by declining activity rates as stored records and tapes are becoming less active and more archival.', |
| | 'How is Iron Mountain addressing the decline in activity rates in their Records and Data Management services?', |
| | 'What services do companies that build fiber-based networks provide in the Connectivity & Platforms markets?', |
| | ] |
| | 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 | 0.7057 | |
| | | cosine_accuracy@3 | 0.8457 | |
| | | cosine_accuracy@5 | 0.8786 | |
| | | cosine_accuracy@10 | 0.9114 | |
| | | cosine_precision@1 | 0.7057 | |
| | | cosine_precision@3 | 0.2819 | |
| | | cosine_precision@5 | 0.1757 | |
| | | cosine_precision@10 | 0.0911 | |
| | | cosine_recall@1 | 0.7057 | |
| | | cosine_recall@3 | 0.8457 | |
| | | cosine_recall@5 | 0.8786 | |
| | | cosine_recall@10 | 0.9114 | |
| | | cosine_ndcg@10 | 0.8125 | |
| | | cosine_mrr@10 | 0.7804 | |
| | | **cosine_map@100** | **0.7839** | |
| | |
| | #### 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 | 0.7071 | |
| | | cosine_accuracy@3 | 0.8429 | |
| | | cosine_accuracy@5 | 0.8743 | |
| | | cosine_accuracy@10 | 0.9114 | |
| | | cosine_precision@1 | 0.7071 | |
| | | cosine_precision@3 | 0.281 | |
| | | cosine_precision@5 | 0.1749 | |
| | | cosine_precision@10 | 0.0911 | |
| | | cosine_recall@1 | 0.7071 | |
| | | cosine_recall@3 | 0.8429 | |
| | | cosine_recall@5 | 0.8743 | |
| | | cosine_recall@10 | 0.9114 | |
| | | cosine_ndcg@10 | 0.8127 | |
| | | cosine_mrr@10 | 0.7807 | |
| | | **cosine_map@100** | **0.7841** | |
| | |
| | #### 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 | 0.7029 | |
| | | cosine_accuracy@3 | 0.8357 | |
| | | cosine_accuracy@5 | 0.8686 | |
| | | cosine_accuracy@10 | 0.9071 | |
| | | cosine_precision@1 | 0.7029 | |
| | | cosine_precision@3 | 0.2786 | |
| | | cosine_precision@5 | 0.1737 | |
| | | cosine_precision@10 | 0.0907 | |
| | | cosine_recall@1 | 0.7029 | |
| | | cosine_recall@3 | 0.8357 | |
| | | cosine_recall@5 | 0.8686 | |
| | | cosine_recall@10 | 0.9071 | |
| | | cosine_ndcg@10 | 0.8087 | |
| | | cosine_mrr@10 | 0.7769 | |
| | | **cosine_map@100** | **0.7806** | |
| | |
| | #### 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 | 0.6914 | |
| | | cosine_accuracy@3 | 0.82 | |
| | | cosine_accuracy@5 | 0.8557 | |
| | | cosine_accuracy@10 | 0.9014 | |
| | | cosine_precision@1 | 0.6914 | |
| | | cosine_precision@3 | 0.2733 | |
| | | cosine_precision@5 | 0.1711 | |
| | | cosine_precision@10 | 0.0901 | |
| | | cosine_recall@1 | 0.6914 | |
| | | cosine_recall@3 | 0.82 | |
| | | cosine_recall@5 | 0.8557 | |
| | | cosine_recall@10 | 0.9014 | |
| | | cosine_ndcg@10 | 0.7981 | |
| | | cosine_mrr@10 | 0.765 | |
| | | **cosine_map@100** | **0.7689** | |
| | |
| | #### 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 | 0.6543 | |
| | | cosine_accuracy@3 | 0.7886 | |
| | | cosine_accuracy@5 | 0.8329 | |
| | | cosine_accuracy@10 | 0.8829 | |
| | | cosine_precision@1 | 0.6543 | |
| | | cosine_precision@3 | 0.2629 | |
| | | cosine_precision@5 | 0.1666 | |
| | | cosine_precision@10 | 0.0883 | |
| | | cosine_recall@1 | 0.6543 | |
| | | cosine_recall@3 | 0.7886 | |
| | | cosine_recall@5 | 0.8329 | |
| | | cosine_recall@10 | 0.8829 | |
| | | cosine_ndcg@10 | 0.769 | |
| | | cosine_mrr@10 | 0.7325 | |
| | | **cosine_map@100** | **0.7369** | |
| | |
| | <!-- |
| | ## 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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| | <!-- |
| | ### Recommendations |
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| | *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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| | |
| | ## 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: 10 tokens</li><li>mean: 46.55 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 20.56 tokens</li><li>max: 42 tokens</li></ul> | |
| | * Samples: |
| | | positive | anchor | |
| | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------| |
| | | <code>Internationally, Visa Inc.'s commercial payments volume grew by 23% from $407 billion in 2021 to $500 billion in 2022.</code> | <code>What was the growth rate of Visa Inc.'s commercial payments volume internationally between 2021 and 2022?</code> | |
| | | <code>The consolidated financial statements and accompanying notes listed in Part IV, Item 15(a)(1) of this Annual Report on Form 10-K are included immediately following Part IV hereof.</code> | <code>Where can one find the consolidated financial statements and accompanying notes in the Annual Report on Form 10-K?</code> | |
| | | <code>The additional paid-in capital at the end of 2023 was recorded as $114,519 million.</code> | <code>What was the amount recorded for additional paid-in capital at the end of 2023?</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`: 80 |
| | - `per_device_eval_batch_size`: 16 |
| | - `gradient_accumulation_steps`: 16 |
| | - `learning_rate`: 2e-05 |
| | - `num_train_epochs`: 15 |
| | - `lr_scheduler_type`: cosine |
| | - `warmup_ratio`: 0.1 |
| | - `bf16`: True |
| | - `tf32`: 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`: 80 |
| | - `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`: 15 |
| | - `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`: 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_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 | |
| | |:-------:|:----:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| |
| | | 0.8101 | 4 | - | 0.7066 | 0.7309 | 0.7390 | 0.6462 | 0.7441 | |
| | | 1.8228 | 9 | - | 0.7394 | 0.7497 | 0.7630 | 0.6922 | 0.7650 | |
| | | 2.0253 | 10 | 2.768 | - | - | - | - | - | |
| | | 2.8354 | 14 | - | 0.7502 | 0.7625 | 0.7767 | 0.7208 | 0.7787 | |
| | | 3.8481 | 19 | - | 0.7553 | 0.7714 | 0.7804 | 0.7234 | 0.7802 | |
| | | 4.0506 | 20 | 1.1294 | - | - | - | - | - | |
| | | 4.8608 | 24 | - | 0.7577 | 0.7769 | 0.7831 | 0.7327 | 0.7858 | |
| | | 5.8734 | 29 | - | 0.7616 | 0.7775 | 0.7832 | 0.7335 | 0.7876 | |
| | | 6.0759 | 30 | 0.7536 | - | - | - | - | - | |
| | | 6.8861 | 34 | - | 0.7624 | 0.7788 | 0.7832 | 0.7352 | 0.7882 | |
| | | 7.8987 | 39 | - | 0.7665 | 0.7795 | 0.7814 | 0.7359 | 0.7861 | |
| | | 8.1013 | 40 | 0.5846 | - | - | - | - | - | |
| | | 8.9114 | 44 | - | 0.7688 | 0.7801 | 0.7828 | 0.7360 | 0.7857 | |
| | | 9.9241 | 49 | - | 0.7698 | 0.7804 | 0.7836 | 0.7367 | 0.7840 | |
| | | 10.1266 | 50 | 0.5187 | - | - | - | - | - | |
| | | 10.9367 | 54 | - | 0.7692 | 0.7801 | 0.7827 | 0.7383 | 0.7837 | |
| | | 11.9494 | 59 | - | 0.7698 | 0.7801 | 0.7834 | 0.7377 | 0.7849 | |
| | | 12.1519 | 60 | 0.4949 | 0.7689 | 0.7806 | 0.7841 | 0.7369 | 0.7839 | |
| | |
| | |
| | ### Framework Versions |
| | - Python: 3.10.12 |
| | - Sentence Transformers: 3.0.1 |
| | - Transformers: 4.41.2 |
| | - PyTorch: 2.2.0+cu121 |
| | - Accelerate: 0.31.0 |
| | - 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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