--- base_model: BAAI/bge-small-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: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: BAAI BGE small en v1.5 ESG results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 384 type: dim_384 metrics: - type: cosine_accuracy@1 value: 0.7661637022675546 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9170530220011801 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9370311051167496 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9542274298238219 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7661637022675546 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.30568434066706 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.18740622102334994 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09542274298238222 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.021282325062987634 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.025473695055588344 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.026028641808798603 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.026506317495106176 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.19177581579273692 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.843606136995247 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.023463069757038203 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.7621175082188316 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9118266880215797 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9353451909297816 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9527944027648992 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7621175082188316 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3039422293405265 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.18706903818595635 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09527944027648994 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.02116993078385644 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.025328519111710558 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.025981810859160608 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.026466511187913874 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.19114210787645763 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8402866254821924 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.023374206451884923 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.7469442805361207 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.898423670235185 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9232066087836129 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9444491275394082 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7469442805361207 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2994745567450616 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1846413217567226 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09444491275394083 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.020748452237114468 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.02495621306208848 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.025644628021767035 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.02623469798720579 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.1883811701569402 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8264706590720244 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.02300099952981619 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.7106128298069628 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8668970749388856 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8978336002697462 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9243867487144904 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7106128298069628 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.28896569164629515 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17956672005394925 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09243867487144905 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.01973924527241564 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.02408047430385794 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.02493982222971518 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.02567740968651363 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.1818069773338387 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7936283816963235 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.022106633007589808 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 32 type: dim_32 metrics: - type: cosine_accuracy@1 value: 0.6166231138835033 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7788923543791622 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8194385905757396 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8608277838658013 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6166231138835033 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.259630784793054 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16388771811514793 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08608277838658013 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.017128419830097316 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.02163589873275451 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.022762183071548335 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.02391188288516115 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.16371507022328244 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7058398528705336 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.019714839230632157 name: Cosine Map@100 --- # BAAI BGE small en v1.5 ESG This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5). 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:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) - **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': True}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, '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("elsayovita/bge-small-en-v1.5-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 | 0.7662 | | cosine_accuracy@3 | 0.9171 | | cosine_accuracy@5 | 0.937 | | cosine_accuracy@10 | 0.9542 | | cosine_precision@1 | 0.7662 | | cosine_precision@3 | 0.3057 | | cosine_precision@5 | 0.1874 | | cosine_precision@10 | 0.0954 | | cosine_recall@1 | 0.0213 | | cosine_recall@3 | 0.0255 | | cosine_recall@5 | 0.026 | | cosine_recall@10 | 0.0265 | | cosine_ndcg@10 | 0.1918 | | cosine_mrr@10 | 0.8436 | | **cosine_map@100** | **0.0235** | #### 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 | 0.7621 | | cosine_accuracy@3 | 0.9118 | | cosine_accuracy@5 | 0.9353 | | cosine_accuracy@10 | 0.9528 | | cosine_precision@1 | 0.7621 | | cosine_precision@3 | 0.3039 | | cosine_precision@5 | 0.1871 | | cosine_precision@10 | 0.0953 | | cosine_recall@1 | 0.0212 | | cosine_recall@3 | 0.0253 | | cosine_recall@5 | 0.026 | | cosine_recall@10 | 0.0265 | | cosine_ndcg@10 | 0.1911 | | cosine_mrr@10 | 0.8403 | | **cosine_map@100** | **0.0234** | #### 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 | 0.7469 | | cosine_accuracy@3 | 0.8984 | | cosine_accuracy@5 | 0.9232 | | cosine_accuracy@10 | 0.9444 | | cosine_precision@1 | 0.7469 | | cosine_precision@3 | 0.2995 | | cosine_precision@5 | 0.1846 | | cosine_precision@10 | 0.0944 | | cosine_recall@1 | 0.0207 | | cosine_recall@3 | 0.025 | | cosine_recall@5 | 0.0256 | | cosine_recall@10 | 0.0262 | | cosine_ndcg@10 | 0.1884 | | cosine_mrr@10 | 0.8265 | | **cosine_map@100** | **0.023** | #### 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 | 0.7106 | | cosine_accuracy@3 | 0.8669 | | cosine_accuracy@5 | 0.8978 | | cosine_accuracy@10 | 0.9244 | | cosine_precision@1 | 0.7106 | | cosine_precision@3 | 0.289 | | cosine_precision@5 | 0.1796 | | cosine_precision@10 | 0.0924 | | cosine_recall@1 | 0.0197 | | cosine_recall@3 | 0.0241 | | cosine_recall@5 | 0.0249 | | cosine_recall@10 | 0.0257 | | cosine_ndcg@10 | 0.1818 | | cosine_mrr@10 | 0.7936 | | **cosine_map@100** | **0.0221** | #### 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 | 0.6166 | | cosine_accuracy@3 | 0.7789 | | cosine_accuracy@5 | 0.8194 | | cosine_accuracy@10 | 0.8608 | | cosine_precision@1 | 0.6166 | | cosine_precision@3 | 0.2596 | | cosine_precision@5 | 0.1639 | | cosine_precision@10 | 0.0861 | | cosine_recall@1 | 0.0171 | | cosine_recall@3 | 0.0216 | | cosine_recall@5 | 0.0228 | | cosine_recall@10 | 0.0239 | | cosine_ndcg@10 | 0.1637 | | cosine_mrr@10 | 0.7058 | | **cosine_map@100** | **0.0197** | ## 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`: 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`: 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`: 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 - `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 | 4.3426 | - | - | - | - | - | | 0.8625 | 20 | 2.7083 | - | - | - | - | - | | 1.0350 | 24 | - | 0.0229 | 0.0233 | 0.0195 | 0.0234 | 0.0220 | | 1.2264 | 30 | 2.6835 | - | - | - | - | - | | 1.6577 | 40 | 2.1702 | - | - | - | - | - | | **1.9164** | **46** | **-** | **0.023** | **0.0234** | **0.0197** | **0.0235** | **0.0221** | * 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} } ```