--- base_model: TaylorAI/bge-micro-v2 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:11863 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: In the fiscal year 2022, the emissions were categorized into different scopes, with each scope representing a specific source of emissions sentences: - 'Question: What is NetLink proactive in identifying to be more efficient in? ' - What standard is the Environment, Health, and Safety Management System (EHSMS) audited to by a third-party accredited certification body at the operational assets level of CLI? - What do the different scopes represent in terms of emissions in the fiscal year 2022? - source_sentence: NetLink is committed to protecting the security of all information and information systems, including both end-user data and corporate data. To this end, management ensures that the appropriate IT policies, personal data protection policy, risk mitigation strategies, cyber security programmes, systems, processes, and controls are in place to protect our IT systems and confidential data sentences: - '"What recognition did NetLink receive in FY22?"' - What measures does NetLink have in place to protect the security of all information and information systems, including end-user data and corporate data? - 'Question: What does Disclosure 102-10 discuss regarding the organization and its supply chain?' - source_sentence: In the domain of economic performance, the focus is on the financial health and growth of the organization, ensuring sustainable profitability and value creation for stakeholders sentences: - What does NetLink prioritize by investing in its network to ensure reliability and quality of infrastructure? - What percentage of the total energy was accounted for by heat, steam, and chilled water in 2021 according to the given information? - What is the focus in the domain of economic performance, ensuring sustainable profitability and value creation for stakeholders? - source_sentence: Disclosure 102-41 discusses collective bargaining agreements and is found on page 98 sentences: - What topic is discussed in Disclosure 102-41 on page 98 of the document? - What was the number of cases in 2021, following a decrease from 42 cases in 2020? - What type of data does GRI 101 provide in relation to connecting the nation? - source_sentence: Employee health and well-being has never been more topical than it was in the past year. We understand that people around the world, including our employees, have been increasingly exposed to factors affecting their physical and mental wellbeing. We are committed to creating an environment that supports our employees and ensures they feel valued and have a sense of belonging. We utilised sentences: - What aspect of the standard covers the evaluation of the management approach? - 'Question: What is the company''s commitment towards its employees'' health and well-being based on the provided context information?' - What types of skills does NetLink focus on developing through their training and development opportunities for employees? model-index: - name: BGE micro v2 ESG results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 384 type: dim_384 metrics: - type: cosine_accuracy@1 value: 0.7393576666947652 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8871280451825002 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9143555593020315 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9382955407569755 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7393576666947652 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2957093483941667 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1828711118604063 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09382955407569755 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.020537712963743484 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.024642445699513908 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.02539876553616755 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.026063765021027103 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.18655528566337626 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8176322873975245 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.022756262897092067 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.731602461434713 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8831661468431257 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9111523223467926 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9355137823484785 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.731602461434713 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2943887156143752 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.18223046446935853 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09355137823484787 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.020322290595408698 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.024532392967864608 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.02530978673185536 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.02598649395412441 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.1854736961250685 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8120234114607371 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.022602117473168613 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.7171035994267891 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8735564359774087 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9012897243530305 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.927927168507123 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7171035994267891 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2911854786591362 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1802579448706061 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09279271685071232 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.019919544428521924 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.02426545655492803 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.025035825676473073 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.025775754680753424 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.18301753980732727 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7997301868287288 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.022264162086570314 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.6758829975554245 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8359605496080249 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8713647475343504 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9060945797858889 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6758829975554245 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2786535165360083 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1742729495068701 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.0906094579785889 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.018774527709872903 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.0232211263780007 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.024204576320398637 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.025169293882941365 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.17554680827328792 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7621402212294056 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.02123787521914149 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 32 type: dim_32 metrics: - type: cosine_accuracy@1 value: 0.575908286268229 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7347214026806036 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.780156790019388 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8298069628255922 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.575908286268229 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.24490713422686783 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1560313580038776 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08298069628255922 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.015997452396339696 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.020408927852238995 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.021671021944983007 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.02305019341182201 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.1551668722356578 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.6648409286443452 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.01858718928494409 name: Cosine Map@100 --- # BGE micro v2 ESG This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [TaylorAI/bge-micro-v2](https://huggingface.co/TaylorAI/bge-micro-v2). It maps sentences & paragraphs to a 384-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:** [TaylorAI/bge-micro-v2](https://huggingface.co/TaylorAI/bge-micro-v2) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 384 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': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## 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("elsayovita/bge-micro-v2-esg") # Run inference sentences = [ 'Employee health and well-being has never been more topical than it was in the past year. We understand that people around the world, including our employees, have been increasingly exposed to factors affecting their physical and mental wellbeing. We are committed to creating an environment that supports our employees and ensures they feel valued and have a sense of belonging. We utilised', "Question: What is the company's commitment towards its employees' health and well-being based on the provided context information?", 'What types of skills does NetLink focus on developing through their training and development opportunities for employees?', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 384] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Information Retrieval * Dataset: `dim_384` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.7394 | | cosine_accuracy@3 | 0.8871 | | cosine_accuracy@5 | 0.9144 | | cosine_accuracy@10 | 0.9383 | | cosine_precision@1 | 0.7394 | | cosine_precision@3 | 0.2957 | | cosine_precision@5 | 0.1829 | | cosine_precision@10 | 0.0938 | | cosine_recall@1 | 0.0205 | | cosine_recall@3 | 0.0246 | | cosine_recall@5 | 0.0254 | | cosine_recall@10 | 0.0261 | | cosine_ndcg@10 | 0.1866 | | cosine_mrr@10 | 0.8176 | | **cosine_map@100** | **0.0228** | #### 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 | 0.7316 | | cosine_accuracy@3 | 0.8832 | | cosine_accuracy@5 | 0.9112 | | cosine_accuracy@10 | 0.9355 | | cosine_precision@1 | 0.7316 | | cosine_precision@3 | 0.2944 | | cosine_precision@5 | 0.1822 | | cosine_precision@10 | 0.0936 | | cosine_recall@1 | 0.0203 | | cosine_recall@3 | 0.0245 | | cosine_recall@5 | 0.0253 | | cosine_recall@10 | 0.026 | | cosine_ndcg@10 | 0.1855 | | cosine_mrr@10 | 0.812 | | **cosine_map@100** | **0.0226** | #### 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 | 0.7171 | | cosine_accuracy@3 | 0.8736 | | cosine_accuracy@5 | 0.9013 | | cosine_accuracy@10 | 0.9279 | | cosine_precision@1 | 0.7171 | | cosine_precision@3 | 0.2912 | | cosine_precision@5 | 0.1803 | | cosine_precision@10 | 0.0928 | | cosine_recall@1 | 0.0199 | | cosine_recall@3 | 0.0243 | | cosine_recall@5 | 0.025 | | cosine_recall@10 | 0.0258 | | cosine_ndcg@10 | 0.183 | | cosine_mrr@10 | 0.7997 | | **cosine_map@100** | **0.0223** | #### 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 | 0.6759 | | cosine_accuracy@3 | 0.836 | | cosine_accuracy@5 | 0.8714 | | cosine_accuracy@10 | 0.9061 | | cosine_precision@1 | 0.6759 | | cosine_precision@3 | 0.2787 | | cosine_precision@5 | 0.1743 | | cosine_precision@10 | 0.0906 | | cosine_recall@1 | 0.0188 | | cosine_recall@3 | 0.0232 | | cosine_recall@5 | 0.0242 | | cosine_recall@10 | 0.0252 | | cosine_ndcg@10 | 0.1755 | | cosine_mrr@10 | 0.7621 | | **cosine_map@100** | **0.0212** | #### Information Retrieval * Dataset: `dim_32` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.5759 | | cosine_accuracy@3 | 0.7347 | | cosine_accuracy@5 | 0.7802 | | cosine_accuracy@10 | 0.8298 | | cosine_precision@1 | 0.5759 | | cosine_precision@3 | 0.2449 | | cosine_precision@5 | 0.156 | | cosine_precision@10 | 0.083 | | cosine_recall@1 | 0.016 | | cosine_recall@3 | 0.0204 | | cosine_recall@5 | 0.0217 | | cosine_recall@10 | 0.0231 | | cosine_ndcg@10 | 0.1552 | | cosine_mrr@10 | 0.6648 | | **cosine_map@100** | **0.0186** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 11,863 training samples * Columns: context and question * Approximate statistics based on the first 1000 samples: | | context | question | |:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | context | question | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------| | The engagement with key stakeholders involves various topics and methods throughout the year | Question: What does the engagement with key stakeholders involve throughout the year? | | For unitholders and analysts, the focus is on business and operations, the release of financial results, and the overall performance and announcements | Question: What is the focus for unitholders and analysts in terms of business and operations, financial results, performance, and announcements? | | These are communicated through press releases and other required disclosures via SGXNet and NetLink's website | What platform is used to communicate press releases and required disclosures for NetLink? | * Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 384, 256, 128, 64, 32 ], "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`: 2 - `lr_scheduler_type`: cosine - `warmup_ratio`: 0.1 - `bf16`: True - `tf32`: False - `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`: 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`: 2 - `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`: False - `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 - `eval_on_start`: 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_32_cosine_map@100 | dim_384_cosine_map@100 | dim_64_cosine_map@100 | |:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|:---------------------:| | 0.4313 | 10 | 5.0772 | - | - | - | - | - | | 0.8625 | 20 | 3.2666 | - | - | - | - | - | | 1.0350 | 24 | - | 0.0221 | 0.0224 | 0.0185 | 0.0226 | 0.0211 | | 1.2264 | 30 | 3.1157 | - | - | - | - | - | | 1.6577 | 40 | 2.585 | - | - | - | - | - | | **1.9164** | **46** | **-** | **0.0223** | **0.0226** | **0.0186** | **0.0228** | **0.0212** | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.1 - Transformers: 4.42.4 - PyTorch: 2.4.0+cu121 - Accelerate: 0.32.1 - Datasets: 2.21.0 - 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} } ```