--- 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: ITEM 7. MANAGEMENT’S DISCUSSION AND ANALYSIS OF FINANCIAL CONDITION AND RESULTS OF OPERATIONS The following discussion and analysis should be read in conjunction with the consolidated financial statements and the related notes included elsewhere in this Annual Report on Form 10-K. For further discussion of our products and services, technology and competitive strengths, refer to Item 1- Business. sentences: - What was the total net automotive cash provided by investing activities in 2023? - What is the purpose of the Management's Discussion and Analysis of Financial Condition and Results of Operations section in the Annual Report on Form 10-K? - What are the components included in the management discussion and analysis of financial condition and results of operations? - source_sentence: Kroger is committed to maintaining a net total debt to adjusted EBITDA ratio target range of 2.30 to 2.50. sentences: - What was the remaining available amount of the share repurchase authorization as of January 29, 2023? - What range does Kroger aim for its net total debt to adjusted EBITDA ratio? - What was the starting wage for all entry-level positions in the U.S. as of September 2023? - source_sentence: Google Cloud operating income of $1.7 billion for 2023. sentences: - What was the operating income for Google Cloud in 2023? - What types of products are offered in Garmin's Fitness segment? - What was the net sales of the company in fiscal 2022? - source_sentence: The effective income tax rate for Alphabet Inc. at the end of the year 2023 was 13.9%. sentences: - What was the percentage change in Compute & Networking revenue from fiscal year 2022 to 2023? - What factors primarily contributed to the increase in non-interest revenues across all revenue categories? - What was the effective income tax rate for Alphabet Inc. at the end of the year 2023? - source_sentence: State legislation increasingly requires PBMs to conduct audits of network pharmacies regarding claims submitted for payment. Non-compliance could prevent the recoupment of overpaid amounts, potentially causing financial and legal repercussions. sentences: - What are the potential consequences for a company if its PBMs fail to comply with pharmacy audit regulations? - What pages do the Consolidated Financial Statements and their accompanying Notes and reports appear on in the document? - What are the primary services provided by the company under the Xfinity, Comcast Business, and Sky brands? 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: 0.6785714285714286 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8342857142857143 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.88 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9085714285714286 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6785714285714286 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2780952380952381 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.176 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09085714285714284 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6785714285714286 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8342857142857143 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.88 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9085714285714286 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7995179593313807 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7638202947845802 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7674168947978975 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.6685714285714286 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8271428571428572 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8685714285714285 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9128571428571428 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6685714285714286 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2757142857142857 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1737142857142857 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09128571428571428 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6685714285714286 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8271428571428572 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8685714285714285 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9128571428571428 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7954721927324272 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7574353741496596 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7606771546726785 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.6728571428571428 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8142857142857143 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8642857142857143 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9042857142857142 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6728571428571428 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2714285714285714 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17285714285714285 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09042857142857141 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6728571428571428 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8142857142857143 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8642857142857143 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9042857142857142 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7916203877025221 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7552613378684805 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7590698804335085 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.6528571428571428 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8114285714285714 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.85 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8885714285714286 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6528571428571428 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2704761904761904 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16999999999999998 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08885714285714286 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6528571428571428 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8114285714285714 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.85 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8885714285714286 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7754227314755763 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.738630385487528 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7431237490151862 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.6157142857142858 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7614285714285715 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.81 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8642857142857143 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6157142857142858 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2538095238095238 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16199999999999998 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08642857142857142 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6157142857142858 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7614285714285715 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.81 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8642857142857143 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7413954849024657 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.701954648526077 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.707051130510896 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("gauravsirola/bge-base-financial-matryoshka-v1") # Run inference sentences = [ 'State legislation increasingly requires PBMs to conduct audits of network pharmacies regarding claims submitted for payment. Non-compliance could prevent the recoupment of overpaid amounts, potentially causing financial and legal repercussions.', 'What are the potential consequences for a company if its PBMs fail to comply with pharmacy audit regulations?', 'What pages do the Consolidated Financial Statements and their accompanying Notes and reports appear on in the document?', ] 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 | 0.6786 | | cosine_accuracy@3 | 0.8343 | | cosine_accuracy@5 | 0.88 | | cosine_accuracy@10 | 0.9086 | | cosine_precision@1 | 0.6786 | | cosine_precision@3 | 0.2781 | | cosine_precision@5 | 0.176 | | cosine_precision@10 | 0.0909 | | cosine_recall@1 | 0.6786 | | cosine_recall@3 | 0.8343 | | cosine_recall@5 | 0.88 | | cosine_recall@10 | 0.9086 | | cosine_ndcg@10 | 0.7995 | | cosine_mrr@10 | 0.7638 | | **cosine_map@100** | **0.7674** | #### 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 | 0.6686 | | cosine_accuracy@3 | 0.8271 | | cosine_accuracy@5 | 0.8686 | | cosine_accuracy@10 | 0.9129 | | cosine_precision@1 | 0.6686 | | cosine_precision@3 | 0.2757 | | cosine_precision@5 | 0.1737 | | cosine_precision@10 | 0.0913 | | cosine_recall@1 | 0.6686 | | cosine_recall@3 | 0.8271 | | cosine_recall@5 | 0.8686 | | cosine_recall@10 | 0.9129 | | cosine_ndcg@10 | 0.7955 | | cosine_mrr@10 | 0.7574 | | **cosine_map@100** | **0.7607** | #### 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 | 0.6729 | | cosine_accuracy@3 | 0.8143 | | cosine_accuracy@5 | 0.8643 | | cosine_accuracy@10 | 0.9043 | | cosine_precision@1 | 0.6729 | | cosine_precision@3 | 0.2714 | | cosine_precision@5 | 0.1729 | | cosine_precision@10 | 0.0904 | | cosine_recall@1 | 0.6729 | | cosine_recall@3 | 0.8143 | | cosine_recall@5 | 0.8643 | | cosine_recall@10 | 0.9043 | | cosine_ndcg@10 | 0.7916 | | cosine_mrr@10 | 0.7553 | | **cosine_map@100** | **0.7591** | #### 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 | 0.6529 | | cosine_accuracy@3 | 0.8114 | | cosine_accuracy@5 | 0.85 | | cosine_accuracy@10 | 0.8886 | | cosine_precision@1 | 0.6529 | | cosine_precision@3 | 0.2705 | | cosine_precision@5 | 0.17 | | cosine_precision@10 | 0.0889 | | cosine_recall@1 | 0.6529 | | cosine_recall@3 | 0.8114 | | cosine_recall@5 | 0.85 | | cosine_recall@10 | 0.8886 | | cosine_ndcg@10 | 0.7754 | | cosine_mrr@10 | 0.7386 | | **cosine_map@100** | **0.7431** | #### 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 | 0.6157 | | cosine_accuracy@3 | 0.7614 | | cosine_accuracy@5 | 0.81 | | cosine_accuracy@10 | 0.8643 | | cosine_precision@1 | 0.6157 | | cosine_precision@3 | 0.2538 | | cosine_precision@5 | 0.162 | | cosine_precision@10 | 0.0864 | | cosine_recall@1 | 0.6157 | | cosine_recall@3 | 0.7614 | | cosine_recall@5 | 0.81 | | cosine_recall@10 | 0.8643 | | cosine_ndcg@10 | 0.7414 | | cosine_mrr@10 | 0.702 | | **cosine_map@100** | **0.7071** | ## 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 | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------| | Net loss was $396.6 million and $973.6 million during the years ended December 31, 2023, and December 31, 2022, respectively. | What was the net loss for the year ended December 31, 2022? | | Under the 2023 IDA agreement, the service fee on client cash deposits held at the TD Depository Institutions remains at 15 basis points, as it was in the 2019 IDA agreement. | How much is the service fee on client cash deposits held at the TD Depository Institutions under the 2023 IDA agreement? | | The total shareholders’ deficit is listed as $7,994.8 million in the latest financial statement. | What is the total shareholder's deficit according to the latest financial statement? | * 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 - `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 - `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 | |:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| | 0.8122 | 10 | 1.5585 | - | - | - | - | - | | 0.9746 | 12 | - | 0.7207 | 0.7441 | 0.7510 | 0.6857 | 0.7493 | | 1.6244 | 20 | 0.6691 | - | - | - | - | - | | 1.9492 | 24 | - | 0.7392 | 0.7564 | 0.7601 | 0.7006 | 0.7661 | | 2.4365 | 30 | 0.4702 | - | - | - | - | - | | 2.9239 | 36 | - | 0.7430 | 0.7600 | 0.7619 | 0.7065 | 0.7685 | | 3.2487 | 40 | 0.407 | - | - | - | - | - | | **3.8985** | **48** | **-** | **0.7431** | **0.7591** | **0.7607** | **0.7071** | **0.7674** | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.6 - Sentence Transformers: 3.0.1 - Transformers: 4.41.2 - PyTorch: 2.1.2+cu121 - Accelerate: 0.31.0 - Datasets: 2.19.1 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```