--- 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: Tesla has implemented various remedial measures, including conducting training and audits, and enhancements to its site waste management programs, and settlement discussions are ongoing. sentences: - What regulatory body primarily regulates product safety, efficacy, and other aspects in the U.S.? - What remedial measures has Tesla implemented in response to the investigation of its waste segregation practices? - What were the main drivers behind the sales growth of TREMFYA? - source_sentence: Sales of Alphagan/Combigan in the United States decreased by 40.1% from $373 million in 2021 to $121 million in 2023. sentences: - What were the total revenues from unaffiliated customers in 2021? - What was the percentage decrease in sales for Alphagan/Combigan in the United States from 2021 to 2023? - What percent excess of fair value over carrying value did the Compute reporting unit have as of the annual test date in 2023? - source_sentence: Long-lived and intangible assets are reviewed for impairment based on indicators of impairment and the evaluation involves estimating the future undiscounted cash flows attributable to the asset groups. sentences: - How are long-lived and intangible assets evaluated for impairment? - What strategies are being adopted to enhance revenue through acquisition according to the business plans described? - How is impairment evaluated for long-lived assets such as leases, property, and equipment? - source_sentence: Our 2023 operating income was $5.5 billion, an improvement of $1.9 billion compared to 2022. sentences: - What was the total unrecognized compensation cost related to unvested stock-based awards as of October 29, 2023? - What significant financial activity occurred in continuing investing activities in 2023? - What was the operating income for 2023, and how did it compare to 2022? - source_sentence: We use raw materials that are subject to price volatility caused by weather, supply conditions, political and economic variables and other unpredictable factors. We may use futures, options and swap contracts to manage the volatility related to the above exposures. sentences: - What financial instruments does the company use to manage commodity price exposure? - What types of legal proceedings is the company currently involved in? - What was the net impact of fair value hedging instruments on earnings in 2023? model-index: - name: BGE base Financial Matryoshka results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 768 type: dim_768 metrics: - type: cosine_accuracy@1 value: 0.6814285714285714 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.82 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8614285714285714 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8942857142857142 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6814285714285714 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2733333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17228571428571426 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08942857142857141 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6814285714285714 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.82 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8614285714285714 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8942857142857142 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7922308461157294 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7589693877551015 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7633405151451278 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.68 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8214285714285714 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8614285714285714 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8957142857142857 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.68 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2738095238095238 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17228571428571426 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08957142857142855 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.68 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8214285714285714 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8614285714285714 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8957142857142857 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7914243245771438 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7576258503401355 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7617439775393929 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.69 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8271428571428572 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8571428571428571 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8928571428571429 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.69 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2757142857142857 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1714285714285714 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08928571428571426 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.69 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8271428571428572 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8571428571428571 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8928571428571429 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7943028094464931 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7623684807256232 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7661836876217925 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.6657142857142857 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8042857142857143 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8457142857142858 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8871428571428571 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6657142857142857 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2680952380952381 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16914285714285712 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08871428571428569 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6657142857142857 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8042857142857143 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8457142857142858 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8871428571428571 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7784460550829944 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7434297052154194 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.74745032636981 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.6342857142857142 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7771428571428571 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8157142857142857 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8642857142857143 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6342857142857142 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.259047619047619 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16314285714285712 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08642857142857142 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6342857142857142 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7771428571428571 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8157142857142857 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8642857142857143 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7508028784634385 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7143225623582764 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7188596090649563 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) - **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("korruz/bge-base-financial-matryoshka") # Run inference sentences = [ 'We use raw materials that are subject to price volatility caused by weather, supply conditions, political and economic variables and other unpredictable factors. We may use futures, options and swap contracts to manage the volatility related to the above exposures.', 'What financial instruments does the company use to manage commodity price exposure?', 'What types of legal proceedings is the company currently involved in?', ] 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 | 0.6814 | | cosine_accuracy@3 | 0.82 | | cosine_accuracy@5 | 0.8614 | | cosine_accuracy@10 | 0.8943 | | cosine_precision@1 | 0.6814 | | cosine_precision@3 | 0.2733 | | cosine_precision@5 | 0.1723 | | cosine_precision@10 | 0.0894 | | cosine_recall@1 | 0.6814 | | cosine_recall@3 | 0.82 | | cosine_recall@5 | 0.8614 | | cosine_recall@10 | 0.8943 | | cosine_ndcg@10 | 0.7922 | | cosine_mrr@10 | 0.759 | | **cosine_map@100** | **0.7633** | #### 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 | 0.68 | | cosine_accuracy@3 | 0.8214 | | cosine_accuracy@5 | 0.8614 | | cosine_accuracy@10 | 0.8957 | | cosine_precision@1 | 0.68 | | cosine_precision@3 | 0.2738 | | cosine_precision@5 | 0.1723 | | cosine_precision@10 | 0.0896 | | cosine_recall@1 | 0.68 | | cosine_recall@3 | 0.8214 | | cosine_recall@5 | 0.8614 | | cosine_recall@10 | 0.8957 | | cosine_ndcg@10 | 0.7914 | | cosine_mrr@10 | 0.7576 | | **cosine_map@100** | **0.7617** | #### 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 | 0.69 | | cosine_accuracy@3 | 0.8271 | | cosine_accuracy@5 | 0.8571 | | cosine_accuracy@10 | 0.8929 | | cosine_precision@1 | 0.69 | | cosine_precision@3 | 0.2757 | | cosine_precision@5 | 0.1714 | | cosine_precision@10 | 0.0893 | | cosine_recall@1 | 0.69 | | cosine_recall@3 | 0.8271 | | cosine_recall@5 | 0.8571 | | cosine_recall@10 | 0.8929 | | cosine_ndcg@10 | 0.7943 | | cosine_mrr@10 | 0.7624 | | **cosine_map@100** | **0.7662** | #### 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 | 0.6657 | | cosine_accuracy@3 | 0.8043 | | cosine_accuracy@5 | 0.8457 | | cosine_accuracy@10 | 0.8871 | | cosine_precision@1 | 0.6657 | | cosine_precision@3 | 0.2681 | | cosine_precision@5 | 0.1691 | | cosine_precision@10 | 0.0887 | | cosine_recall@1 | 0.6657 | | cosine_recall@3 | 0.8043 | | cosine_recall@5 | 0.8457 | | cosine_recall@10 | 0.8871 | | cosine_ndcg@10 | 0.7784 | | cosine_mrr@10 | 0.7434 | | **cosine_map@100** | **0.7475** | #### 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 | 0.6343 | | cosine_accuracy@3 | 0.7771 | | cosine_accuracy@5 | 0.8157 | | cosine_accuracy@10 | 0.8643 | | cosine_precision@1 | 0.6343 | | cosine_precision@3 | 0.259 | | cosine_precision@5 | 0.1631 | | cosine_precision@10 | 0.0864 | | cosine_recall@1 | 0.6343 | | cosine_recall@3 | 0.7771 | | cosine_recall@5 | 0.8157 | | cosine_recall@10 | 0.8643 | | cosine_ndcg@10 | 0.7508 | | cosine_mrr@10 | 0.7143 | | **cosine_map@100** | **0.7189** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 6,300 training samples * Columns: positive and anchor * Approximate statistics based on the first 1000 samples: | | positive | anchor | |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | positive | anchor | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------| | The sale and donation transactions closed in June 2022. Total proceeds from the sale were approximately $6,300 (net of transaction and closing costs), resulting in a loss of $13,568, which was recorded in the SM&A expense caption within the Consolidated Statements of Income. | What were Hershey's total proceeds from the sale of a building portion in June 2022, and what was the resulting financial impact? | | Operating income margin increased to 7.9% in fiscal 2022 compared to 6.9% in fiscal 2021. | What was the operating income margin for fiscal year 2022 compared to fiscal year 2021? | | iPhone® is the Company’s line of smartphones based on its iOS operating system. The iPhone line includes iPhone 15 Pro, iPhone 15, iPhone 14, iPhone 13 and iPhone SE®. | What operating system is used for the Company's iPhone line? | * 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 - `gradient_accumulation_steps`: 16 - `learning_rate`: 2e-05 - `num_train_epochs`: 4 - `lr_scheduler_type`: cosine - `warmup_ratio`: 0.1 - `bf16`: True - `tf32`: True - `load_best_model_at_end`: True - `optim`: adamw_torch_fused - `batch_sampler`: no_duplicates #### All Hyperparameters
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 - `torch_empty_cache_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 4 - `max_steps`: -1 - `lr_scheduler_type`: cosine - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: True - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: True - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch_fused - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `eval_use_gather_object`: 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 | |:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| | 0.9697 | 6 | - | 0.7248 | 0.7459 | 0.7534 | 0.6859 | 0.7549 | | 1.6162 | 10 | 2.3046 | - | - | - | - | - | | 1.9394 | 12 | - | 0.7456 | 0.7601 | 0.7590 | 0.7111 | 0.7599 | | 2.9091 | 18 | - | 0.7470 | 0.7652 | 0.7618 | 0.7165 | 0.7622 | | 3.2323 | 20 | 1.0018 | - | - | - | - | - | | **3.8788** | **24** | **-** | **0.7475** | **0.7662** | **0.7617** | **0.7189** | **0.7633** | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.1 - Transformers: 4.44.0 - PyTorch: 2.4.0+cu121 - Accelerate: 0.33.0 - 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} } ```