--- language: - en license: apache-2.0 library_name: sentence-transformers tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:872 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss base_model: BAAI/bge-base-en-v1.5 datasets: [] 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 widget: - source_sentence: 'amendements to PIPA came into force on 05 Auguest 2020. 2 Some parts of PIPA also apply to online service providers. 3 The latest amendment to PIPA has introduced the concept of ‘pseudonymised data’ for the feasibility of data economy. 4 Under the PIPA, all data handlers must appoint a chief privacy officer. 5 Cookies, IP information, etc. are also regulated by the PIPA as personal information. 6 Breach of a corrective order issued by the PIPC can lead to an administrative fine of not more than KRW 30 million. ### Forrester Names Securiti a Leader in the Privacy Management Wave Q4, 2021 Read the Report ### Securiti named a Leader in the IDC MarketScape for Data Privacy Compliance Software Read the Report At' sentences: - What recognition did Securiti receive in the field of data privacy? - How does the Office of the Privacy Commissioner educate agencies and organisations in breach of the law? - What is the concept of 'pseudonymised data' introduced by the latest amendment to PIPA? - source_sentence: '18th, 2020, and it has been in effect since then. ## Influence of GDPR It is well known that the LGPD was drafted and based on the GDPR, so much so that some people call it Brazil’s GDPR. The LGPD contains 65 articles that provide individuals with data subject rights, impose obligations upon organizations for lawful processing of personal data, require notification of data breaches to the supervisory authority and affected data subjects, create a national supervisory authority to interpret and enforce the law, regulate international transfer of data, define lawful consent collection guidelines and impose heavy penalties on violators similar to the GDPR. ## Essence of the LGPD Law LGPD provides: 9 data subject rights requests exercisable by individual data subjects; 10 legal bases for lawful processing; Obligatory and transparent disclosure requirements for organizations to contain within their privacy policy; Consent collection and management requirements for organizations;' sentences: - What are the penalties for misusing personal data and obstructing investigations under the PDPA and its amendments? - Which data privacy regulation, similar to the GDPR, had a significant impact in the US after the promulgation of the GDPR in the EU? - What are the requirements for consent collection and management under the LGPD law? - source_sentence: 'to the Privacy Act of 2020. ## Obligations for Organisations Under the Privacy Act 2020 Under the Privacy Act’s jurisdiction, all organizations have specific responsibilities or obligations towards their users. The most important of these obligations include the following: ### 1\. Lawful Purpose Requirements While data processing has become immensely important for nearly all businesses, the Privacy Act ensures that such data processing can only occur if the organization collecting the data has a lawful purpose for the collection and that collection of the information is necessary for that purpose. It is also expected that the information will be collected directly from the individual concerned. When collecting personal information, organizations are required to ensure the individual is aware of: The fact that the information is being collected; The purpose for which it is being collected; The intended recipients of the information; The details of the organization that will be collecting and holding the information; Any laws that authorize or' sentences: - What are the obligations of organizations towards users under the Privacy Act of 2020, including lawful purpose and consent requirements? - What is the role of the Spanish Data Protection Agency in enforcing data protection legislation in Spain and how does it ensure its effectiveness in enforcing the law across the country? - What is the purpose of Kuwait's Data Privacy Protection Regulation (DPPR)? - source_sentence: '## Right of Access to Personal Data: What To Know The wealth of data available to organizations globally has brought tremendous improvements in their ability to target and cater to their customers'' needs. Organizations... View More September 13, 2023 ## Kuwait''s DPPR Kuwait didn’t have any data protection law until the Communication and Information Technology Regulatory Authority (CITRA) introduced the Data Privacy Protection Regulation (DPPR). The... ## Take a Product Tour See how easy it is to manage privacy compliance with robotic automation. Watch a demo At Securiti, our mission is to enable enterprises to safely harness the incredible power of data and the cloud by controlling the complex security, privacy and compliance risks. Copyright (C) 2023 Securiti Sitemap XML Sitemap #### Newsletter #### Company About Us , Personal Data: What To Know The wealth of data available to organizations globally has brought tremendous improvements in their ability to target and cater to their customers'' needs. Organizations... View More September 13, 2023 ## Kuwait''s DPPR Kuwait didn’t have any data protection law until the Communication and Information Technology Regulatory Authority (CITRA) introduced the Data Privacy Protection Regulation (DPPR). The... ## Take a Product Tour See how easy it is to manage privacy compliance with robotic automation. Watch a demo At Securiti, our mission is to enable enterprises to safely harness the incredible power of data and the cloud by controlling the complex security, privacy and compliance risks. Copyright (C) 2023 Securiti Sitemap XML Sitemap #### Newsletter #### Company About Us Careers Contact Us' sentences: - What is the definition of personal data according to the PDPO? - What are the requirements for organizations to notify the regulatory authority in case of a data breach according to the PDPL and accompanying Regulations? - Why did CITRA introduce Kuwait's DPPR? - source_sentence: View Salesforce View Workday View GCP View Azure View Oracle View Learn more Regulations Automate compliance with global privacy regulations. US California CCPA View US California CPRA View European Union GDPR View Thailand’s PDPA View China PIPL View Canada PIPEDA View Brazil's LGPD View \+ More View Learn more Roles Identify data risk and enable protection & control. Privacy View Security View Governance View Marketing View Resources Blog Read through our articles written by industry experts Collateral Product broch sentences: - What resources are available for learning more about GCP? - What are the penalties for unauthorized personal data transfer, including maximum fines for data fiduciaries in various scenarios? - What are the key provisions of South Korea's data privacy law? pipeline_tag: sentence-similarity model-index: - name: SentenceTransformer based on BAAI/bge-base-en-v1.5 results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 768 type: dim_768 metrics: - type: cosine_accuracy@1 value: 0.36082474226804123 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.5463917525773195 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.5773195876288659 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.6907216494845361 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.36082474226804123 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.18213058419243983 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.11546391752577319 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.0690721649484536 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.36082474226804123 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.5463917525773195 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.5773195876288659 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.6907216494845361 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5180083093560761 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.46394207167403045 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.47681473846718614 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.36082474226804123 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.5360824742268041 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.5773195876288659 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.7010309278350515 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.36082474226804123 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.17869415807560135 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.11546391752577319 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.07010309278350516 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.36082474226804123 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.5360824742268041 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.5773195876288659 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.7010309278350515 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5187124999739344 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.4620520373097693 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.4737872459927759 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.32989690721649484 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.4948453608247423 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.5773195876288659 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.6804123711340206 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.32989690721649484 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.1649484536082474 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.11546391752577319 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.06804123711340206 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.32989690721649484 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.4948453608247423 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.5773195876288659 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.6804123711340206 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.4929368061598079 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.43412698412698414 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.44657071536051934 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.3402061855670103 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.5051546391752577 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.5670103092783505 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.6907216494845361 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.3402061855670103 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.1683848797250859 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1134020618556701 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.0690721649484536 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.3402061855670103 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.5051546391752577 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.5670103092783505 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.6907216494845361 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5032662355781912 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.4449517263950254 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.4553038204145196 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.32989690721649484 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.4948453608247423 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.5567010309278351 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.6597938144329897 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.32989690721649484 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.1649484536082474 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.11134020618556702 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.06597938144329896 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.32989690721649484 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.4948453608247423 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.5567010309278351 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.6597938144329897 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.481245330711533 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.42577319587628865 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.43965778950983864 name: Cosine Map@100 --- # SentenceTransformer based on BAAI/bge-base-en-v1.5 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) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 tokens - **Similarity Function:** Cosine Similarity - **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("MugheesAwan11/bge-base-securiti-dataset-1-v12") # Run inference sentences = [ "View Salesforce View Workday View GCP View Azure View Oracle View Learn more Regulations Automate compliance with global privacy regulations. US California CCPA View US California CPRA View European Union GDPR View Thailand’s PDPA View China PIPL View Canada PIPEDA View Brazil's LGPD View \\+ More View Learn more Roles Identify data risk and enable protection & control. Privacy View Security View Governance View Marketing View Resources Blog Read through our articles written by industry experts Collateral Product broch", 'What resources are available for learning more about GCP?', "What are the key provisions of South Korea's data privacy law?", ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Information Retrieval * Dataset: `dim_768` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.3608 | | cosine_accuracy@3 | 0.5464 | | cosine_accuracy@5 | 0.5773 | | cosine_accuracy@10 | 0.6907 | | cosine_precision@1 | 0.3608 | | cosine_precision@3 | 0.1821 | | cosine_precision@5 | 0.1155 | | cosine_precision@10 | 0.0691 | | cosine_recall@1 | 0.3608 | | cosine_recall@3 | 0.5464 | | cosine_recall@5 | 0.5773 | | cosine_recall@10 | 0.6907 | | cosine_ndcg@10 | 0.518 | | cosine_mrr@10 | 0.4639 | | **cosine_map@100** | **0.4768** | #### Information Retrieval * Dataset: `dim_512` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.3608 | | cosine_accuracy@3 | 0.5361 | | cosine_accuracy@5 | 0.5773 | | cosine_accuracy@10 | 0.701 | | cosine_precision@1 | 0.3608 | | cosine_precision@3 | 0.1787 | | cosine_precision@5 | 0.1155 | | cosine_precision@10 | 0.0701 | | cosine_recall@1 | 0.3608 | | cosine_recall@3 | 0.5361 | | cosine_recall@5 | 0.5773 | | cosine_recall@10 | 0.701 | | cosine_ndcg@10 | 0.5187 | | cosine_mrr@10 | 0.4621 | | **cosine_map@100** | **0.4738** | #### Information Retrieval * Dataset: `dim_256` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.3299 | | cosine_accuracy@3 | 0.4948 | | cosine_accuracy@5 | 0.5773 | | cosine_accuracy@10 | 0.6804 | | cosine_precision@1 | 0.3299 | | cosine_precision@3 | 0.1649 | | cosine_precision@5 | 0.1155 | | cosine_precision@10 | 0.068 | | cosine_recall@1 | 0.3299 | | cosine_recall@3 | 0.4948 | | cosine_recall@5 | 0.5773 | | cosine_recall@10 | 0.6804 | | cosine_ndcg@10 | 0.4929 | | cosine_mrr@10 | 0.4341 | | **cosine_map@100** | **0.4466** | #### Information Retrieval * Dataset: `dim_128` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.3402 | | cosine_accuracy@3 | 0.5052 | | cosine_accuracy@5 | 0.567 | | cosine_accuracy@10 | 0.6907 | | cosine_precision@1 | 0.3402 | | cosine_precision@3 | 0.1684 | | cosine_precision@5 | 0.1134 | | cosine_precision@10 | 0.0691 | | cosine_recall@1 | 0.3402 | | cosine_recall@3 | 0.5052 | | cosine_recall@5 | 0.567 | | cosine_recall@10 | 0.6907 | | cosine_ndcg@10 | 0.5033 | | cosine_mrr@10 | 0.445 | | **cosine_map@100** | **0.4553** | #### Information Retrieval * Dataset: `dim_64` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.3299 | | cosine_accuracy@3 | 0.4948 | | cosine_accuracy@5 | 0.5567 | | cosine_accuracy@10 | 0.6598 | | cosine_precision@1 | 0.3299 | | cosine_precision@3 | 0.1649 | | cosine_precision@5 | 0.1113 | | cosine_precision@10 | 0.066 | | cosine_recall@1 | 0.3299 | | cosine_recall@3 | 0.4948 | | cosine_recall@5 | 0.5567 | | cosine_recall@10 | 0.6598 | | cosine_ndcg@10 | 0.4812 | | cosine_mrr@10 | 0.4258 | | **cosine_map@100** | **0.4397** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 872 training samples * Columns: positive and anchor * Approximate statistics based on the first 1000 samples: | | positive | anchor | |:--------|:-------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | positive | anchor | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------| | controller should inform the data subject in every situation where his or her personal data is processed. The LPPD provides a general requirement to provide information on the collection methods but does not explicitly refer to automated decision-making or profiling. vs Articles: 5 14, Recitals: 58 63 This right requires the controller to provide the following information to the data subject when requested. This should be given in a concise, transparent, intelligible, and easily accessible form, using plain language: The identity and contact details of the controller, controller’s representative, and DPO, where applicable The purpose and the legal basis of the processing The categories of personal data concerned The recipients of the personal data The appropriate or suitable safeguards and the means to obtain a copy of them or where they have been made available The controller must provide information necessary to ensure fair and transparent processing whether or not the personal | What information must the controller provide regarding their identity and contact details? | | and deletions, and manage all vendor contracts and compliance documents. ## Key Rights Under Ghana’s Data Protection Act 2012 **Right to be Informed** : Data subjects have the right to be informed of the processing of their personal data and the purposes for which the data is processed. **Right to Access:** Data subjects have the right to obtain confirmation whether or not the controller holds personal data about them, access their personal data, and obtain descriptions of data recipients. **Right to Rectification** : Under the right to rectification, data subjects can request the correction of their data. **Right to Erasure:** Data subjects have the right to request the erasure and destruction of the data that is no longer needed by the organization. **Right to Object:** The data subject has the right to prevent the data controller from processing personal data if such processing causes or is likely to cause unwarranted damage or distress to the data | What are the key rights provided to data subjects under Ghana's Data Protection Act 2012? | | aim to protect personal data, they have differences in scope, requirements, and applicability. PDPA applies to Thailand, while GDPR applies to the European Union. The effect of PDPA in Thailand is to regulate how personal data is processed, collected, used, and protected by individuals and organizations in the country. Thailand's PDPA includes provisions related to personal data breach notifications, requiring data controllers to notify the Personal Data Protection Committee (PDPC) of a personal data breach as soon as possible, preferably within 72 hours of becoming aware of it. The principles of PDPA in Thailand include obtaining consent, especially for minors, ensuring data security, issuing timely data breach notifications, designating a data protection officer, conducting data protection impact assessments, maintaining a record of processing activities, and ensuring adequate standards when transferring data across borders. ## Join Our Newsletter Get all the latest information, law updates and more delivered to your inbox ### Share | What is the role of obtaining consent in Thailand's PDPA? | * Loss: [MatryoshkaLoss](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 - `learning_rate`: 2e-05 - `num_train_epochs`: 10 - `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
Click to expand - `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`: 1 - `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`: 10 - `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
### 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 | |:-------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| | 0.3571 | 10 | 6.8967 | - | - | - | - | - | | 0.7143 | 20 | 6.1128 | - | - | - | - | - | | 1.0 | 28 | - | 0.4344 | 0.4387 | 0.4857 | 0.3831 | 0.4515 | | 1.0714 | 30 | 4.4294 | - | - | - | - | - | | 1.4286 | 40 | 3.2369 | - | - | - | - | - | | 1.7857 | 50 | 3.2624 | - | - | - | - | - | | 2.0 | 56 | - | 0.4345 | 0.4456 | 0.4752 | 0.3885 | 0.4672 | | 2.1429 | 60 | 2.1973 | - | - | - | - | - | | 2.5 | 70 | 1.815 | - | - | - | - | - | | 2.8571 | 80 | 1.8725 | - | - | - | - | - | | **3.0** | **84** | **-** | **0.4636** | **0.4469** | **0.4781** | **0.4012** | **0.4765** | | 3.2143 | 90 | 1.2027 | - | - | - | - | - | | 3.5714 | 100 | 1.3053 | - | - | - | - | - | | 3.9286 | 110 | 1.1 | - | - | - | - | - | | 4.0 | 112 | - | 0.4417 | 0.4282 | 0.4721 | 0.4154 | 0.4671 | | 4.2857 | 120 | 0.8088 | - | - | - | - | - | | 4.6429 | 130 | 0.8744 | - | - | - | - | - | | 5.0 | 140 | 0.8075 | 0.4435 | 0.4443 | 0.4725 | 0.4116 | 0.4720 | | 5.3571 | 150 | 0.5131 | - | - | - | - | - | | 5.7143 | 160 | 0.6387 | - | - | - | - | - | | 6.0 | 168 | - | 0.4495 | 0.4375 | 0.4768 | 0.4363 | 0.4794 | | 6.0714 | 170 | 0.5041 | - | - | - | - | - | | 6.4286 | 180 | 0.4053 | - | - | - | - | - | | 6.7857 | 190 | 0.5665 | - | - | - | - | - | | 7.0 | 196 | - | 0.4549 | 0.4504 | 0.4721 | 0.4382 | 0.4792 | | 7.1429 | 200 | 0.3854 | - | - | - | - | - | | 7.5 | 210 | 0.3085 | - | - | - | - | - | | 7.8571 | 220 | 0.461 | - | - | - | - | - | | 8.0 | 224 | - | 0.4570 | 0.4465 | 0.4722 | 0.4399 | 0.4785 | | 8.2143 | 230 | 0.2521 | - | - | - | - | - | | 8.5714 | 240 | 0.3944 | - | - | - | - | - | | 8.9286 | 250 | 0.3524 | - | - | - | - | - | | 9.0 | 252 | - | 0.4533 | 0.4457 | 0.4736 | 0.4394 | 0.4764 | | 9.2857 | 260 | 0.2825 | - | - | - | - | - | | 9.6429 | 270 | 0.3919 | - | - | - | - | - | | 10.0 | 280 | 0.4004 | 0.4553 | 0.4466 | 0.4738 | 0.4397 | 0.4768 | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.14 - Sentence Transformers: 3.0.1 - Transformers: 4.41.2 - PyTorch: 2.1.2+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} } ```