--- language: - en license: apache-2.0 tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:6300 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss base_model: intfloat/e5-large-unsupervised widget: - source_sentence: What are the key components of the transparency provisions included in the Consolidated Appropriations Act of 2021 regarding healthcare? sentences: - The report includes information on legal proceedings under 'Note 13 — Commitments and Contingencies — Litigation and Other Legal Matters' which is a part of the consolidated financial statements - The Consolidated Appropriations Act of 2021 was signed into law in December 2020 and contains further transparency provisions requiring group health plans and health insurance issuers to report certain prescription drug costs, overall spending on health services and prescription drugs, and information about premiums and the impact of rebates and other remuneration on premiums and out-of-pocket costs to the Tri-Departments. - In 2023, the company recorded other operating charges of $1,951 million. - source_sentence: What technology does the Tax Advisor use and for what purpose in Intuit's offerings? sentences: - In 2023, Goldman Sachs' investments in funds at NAV primarily included firm-sponsored private equity, credit, real estate, and hedge funds. These funds are involved in various types of investments such as leveraged buyouts, recapitalizations, growth investments, and distressed investments for private equity, while credit funds are focused on providing private high-yield capital for leveraged and management buyout transactions. Real estate funds invest globally in real estate assets, and hedge funds adopt a fundamental bottom-up investment approach. - Using AI technologies, our Tax Advisor offering leverages information generated from our ProConnect Tax Online and Lacerte offerings to enable year-round tax planning services and communicate tax savings strategies to clients. - '''Note 13 — Commitments and Contingencies'' provides details about litigation and other legal matters in an Annual Report on Form 10-K.' - source_sentence: What was the net revenue for the Data Center segment in 2023? sentences: - Data Center net revenue of $6.5 billion in 2023 increased by 7%, compared to net revenue of $6.0 billion in 2022. - Under its Class 2 insurance license, Caterpillar Insurance Co. Ltd. insures its parent and affiliates for general liability, property, auto liability and cargo. It also provides reinsurance to CaterThe pillar Insurance Company under a quota share reinsurance agreement for its contractual liability and contractors’ equipment programs in the United States. - Schwab’s funding of these remaining commitments is dependent upon the occurrence of certain conditions, and Schwab expects to pay substantially all of these commitments between 2024 and 2027. - source_sentence: What are the three principles of liquidity risk management at Goldman Sachs? sentences: - The Company determines if an arrangement is a lease at inception and classifies its leases at commencement. Operating leases are included in operating lease right-of-use ("ROU") assets and current and noncurrent operating lease liabilities on the Company’s consolidated balance sheets. - Garmin Ltd. reported a net income of $1,289,636 for the fiscal year ended December 30, 2023. - 'Goldman Sachs manages liquidity risk based on three principles: 1) hold sufficient excess liquidity in the form of GCLA to cover outflows during a stressed period, 2) maintain appropriate Asset-Liability Management, and 3) maintain a viable Contingency Funding Plan.' - source_sentence: What was the total cost and expenses reported by Berkshire Hathaway for the year ended December 31, 2023? sentences: - Total costs and expenses | | 321,144 | | | 266,484 | | | 243,752 - Qulipta (atogepant) is a calcitonin gene-related peptide receptor antagonist indicated for the preventive treatment of episodic and chronic migraine in adults. Qulipta is commercialized in the United States and Canada and is approved in the European Union under the brand name Aquipta. - Item 3 'Legal Proceedings' is integrated by reference to other parts including Note 22 — 'Environmental and legal matters' and Part II, Item 8. pipeline_tag: sentence-similarity library_name: sentence-transformers 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 model-index: - name: E5 unsupervised Financial Matryoshka results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 768 type: dim_768 metrics: - type: cosine_accuracy@1 value: 0.7271428571428571 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.85 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8785714285714286 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9114285714285715 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7271428571428571 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2833333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17571428571428568 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09114285714285714 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7271428571428571 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.85 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8785714285714286 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9114285714285715 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.822517236613446 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7936921768707483 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7973883589026711 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.7271428571428571 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8457142857142858 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.88 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9128571428571428 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7271428571428571 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.28190476190476194 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.176 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09128571428571429 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7271428571428571 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8457142857142858 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.88 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9128571428571428 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8223709830528422 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.793145691609977 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7966990460475021 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.72 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8457142857142858 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8714285714285714 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9057142857142857 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.72 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.28190476190476194 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17428571428571424 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09057142857142855 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.72 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8457142857142858 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8714285714285714 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9057142857142857 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8159991941699124 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7869370748299319 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7906967878713818 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.7085714285714285 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8285714285714286 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8728571428571429 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8985714285714286 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7085714285714285 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2761904761904762 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17457142857142854 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08985714285714284 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7085714285714285 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8285714285714286 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8728571428571429 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8985714285714286 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8073517667504667 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7777108843537414 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7815591417851651 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.6757142857142857 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8185714285714286 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8457142857142858 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8842857142857142 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6757142857142857 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27285714285714285 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16914285714285712 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08842857142857141 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6757142857142857 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8185714285714286 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8457142857142858 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8842857142857142 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7861731335824387 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7542681405895693 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7588497811523153 name: Cosine Map@100 --- # E5 unsupervised Financial Matryoshka This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/e5-large-unsupervised](https://huggingface.co/intfloat/e5-large-unsupervised) on the json dataset. It maps sentences & paragraphs to a 1024-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:** [intfloat/e5-large-unsupervised](https://huggingface.co/intfloat/e5-large-unsupervised) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 1024 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - json - **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': 1024, '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}) (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("schawla2/e5-unsupervised-financial-matryoshka") # Run inference sentences = [ 'What was the total cost and expenses reported by Berkshire Hathaway for the year ended December 31, 2023?', 'Total costs and expenses | | 321,144 | | | 266,484 | | | 243,752', 'Qulipta (atogepant) is a calcitonin gene-related peptide receptor antagonist indicated for the preventive treatment of episodic and chronic migraine in adults. Qulipta is commercialized in the United States and Canada and is approved in the European Union under the brand name Aquipta.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Information Retrieval * Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | dim_768 | dim_512 | dim_256 | dim_128 | dim_64 | |:--------------------|:-----------|:-----------|:----------|:-----------|:-----------| | cosine_accuracy@1 | 0.7271 | 0.7271 | 0.72 | 0.7086 | 0.6757 | | cosine_accuracy@3 | 0.85 | 0.8457 | 0.8457 | 0.8286 | 0.8186 | | cosine_accuracy@5 | 0.8786 | 0.88 | 0.8714 | 0.8729 | 0.8457 | | cosine_accuracy@10 | 0.9114 | 0.9129 | 0.9057 | 0.8986 | 0.8843 | | cosine_precision@1 | 0.7271 | 0.7271 | 0.72 | 0.7086 | 0.6757 | | cosine_precision@3 | 0.2833 | 0.2819 | 0.2819 | 0.2762 | 0.2729 | | cosine_precision@5 | 0.1757 | 0.176 | 0.1743 | 0.1746 | 0.1691 | | cosine_precision@10 | 0.0911 | 0.0913 | 0.0906 | 0.0899 | 0.0884 | | cosine_recall@1 | 0.7271 | 0.7271 | 0.72 | 0.7086 | 0.6757 | | cosine_recall@3 | 0.85 | 0.8457 | 0.8457 | 0.8286 | 0.8186 | | cosine_recall@5 | 0.8786 | 0.88 | 0.8714 | 0.8729 | 0.8457 | | cosine_recall@10 | 0.9114 | 0.9129 | 0.9057 | 0.8986 | 0.8843 | | **cosine_ndcg@10** | **0.8225** | **0.8224** | **0.816** | **0.8074** | **0.7862** | | cosine_mrr@10 | 0.7937 | 0.7931 | 0.7869 | 0.7777 | 0.7543 | | cosine_map@100 | 0.7974 | 0.7967 | 0.7907 | 0.7816 | 0.7588 | ## Training Details ### Training Dataset #### json * Dataset: json * Size: 6,300 training samples * Columns: anchor and positive * Approximate statistics based on the first 1000 samples: | | anchor | positive | |:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | anchor | positive | |:--------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | How many full-time employees did Microsoft report as of June 30, 2023? | As of June 30, 2023, we employed approximately 221,000 people on a full-time basis, 120,000 in the U.S. and 101,000 internationally. | | What was the total amount CSC paid for Series G preferred stock repurchases in 2023? | In 2023, CSC repurchased 42,036 depositary shares representing interests in Series G preferred stock for a total amount of $42 million. | | What does Note 13 in the Annual Report on Form 10-K discuss? | For a discussion of legal and other proceedings in which we are involved, see Note 13 - Commitments and Contingencies in the Notes to Consolidated Financial Statements. | * 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_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`: 8 - `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`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `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 - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 | |:---------:|:-------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:| | 0.2030 | 10 | 9.3166 | - | - | - | - | - | | 0.4061 | 20 | 3.7163 | - | - | - | - | - | | 0.6091 | 30 | 2.8216 | - | - | - | - | - | | 0.8122 | 40 | 1.9313 | - | - | - | - | - | | 1.0 | 50 | 1.5613 | 0.8230 | 0.8237 | 0.8153 | 0.8036 | 0.7771 | | 1.2030 | 60 | 1.0926 | - | - | - | - | - | | 1.4061 | 70 | 0.3367 | - | - | - | - | - | | 1.6091 | 80 | 0.3958 | - | - | - | - | - | | 1.8122 | 90 | 0.6527 | - | - | - | - | - | | 2.0 | 100 | 0.4483 | 0.8202 | 0.8209 | 0.8118 | 0.8033 | 0.7792 | | 2.2030 | 110 | 0.1823 | - | - | - | - | - | | 2.4061 | 120 | 0.0494 | - | - | - | - | - | | 2.6091 | 130 | 0.1204 | - | - | - | - | - | | 2.8122 | 140 | 0.2021 | - | - | - | - | - | | 3.0 | 150 | 0.2088 | 0.8211 | 0.8213 | 0.8148 | 0.8064 | 0.7825 | | 3.2030 | 160 | 0.062 | - | - | - | - | - | | 3.4061 | 170 | 0.022 | - | - | - | - | - | | 3.6091 | 180 | 0.0654 | - | - | - | - | - | | 3.8122 | 190 | 0.1481 | - | - | - | - | - | | **3.934** | **196** | **-** | **0.8225** | **0.8224** | **0.816** | **0.8074** | **0.7862** | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.16 - Sentence Transformers: 3.3.1 - Transformers: 4.48.1 - PyTorch: 2.5.1+cu124 - Accelerate: 1.3.0 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## 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} } ```