--- language: - en license: cc-by-nc-sa-4.0 library_name: sentence-transformers tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:6300 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss base_model: BAAI/bge-base-en-v1.5 datasets: [] 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 widget: - source_sentence: What was the main reason for the decrease in U.S. dialysis treatments in 2023? sentences: - ' •Net earnings decreased modestly by $55 million to $14.7 billion versus year ago as the increase in operating income was more than fully offset by a higher effective tax rate. Foreign exchange impacts reduced net earnings by approximately $1.4 billion. ' - The decrease in U.S. dialysis treatments in 2023 was primarily driven by fewer treatment days. - In the 2023 Annual Report for IBM, the Financial Statements and Supplementary Data are covered on pages 44 through 121. - source_sentence: What credit ratings were assigned to the company by Standard & Poor’s and Moody’s at the end of 2022? sentences: - As of January 28, 2023, the total financial obligations listed for 2027 amounted to $2,210 million according to the summary table. - Our investment-grade credit rating at December 31, 2023 was BBB+ according to Standard & Poor’s Rating Services, or S&P, and Baa2 according to Moody’s Investors Services, Inc., or Moody’s. - Adjusted net earnings of $4.23 per diluted share for 2022 represented an increase of 14.9% compared to adjusted net earnings of $3.68 per diluted share for 2021. - source_sentence: What does qui tam litigation refer to in the context of legal proceedings? sentences: - Qui tam litigation in legal proceedings involves litigation brought by individuals who are attempting to sue on behalf of the government. - The total fair value of awards vested during 2023 was $77,626. - Beginning in the first quarter of fiscal 2025, following the complete implementation of the one FedEx consolidation plan, FedEx will adopt a resegmented structure that will be aligned with how management intends to evaluate performance and allocate resources. - source_sentence: What financial effect does an increase in the discount rate have on intangible asset valuations? sentences: - Beginning in the fourth quarter of 2023, our Family metrics no longer include Messenger Kids users. - We use comparable sales as a metric to evaluate the performance of our business. Refer to the Comparable Sales and Sales Per Square Foot section of this management's discussion and analysis of financial condition and results of operations for further information. - Changes in the discount rate, like an increase, can lead to recognizing an impairment of an intangible asset in spite of achieving forecasted or greater cash flows. - source_sentence: On which pages does the Glossary of Terms and Acronyms appear in the financial document? sentences: - The 'Glossary of Terms and Acronyms' is included on pages 315-321 in the financial document. - Total operating expenses for the fiscal year ended January 31 were $21,962 million in 2023 and $18,918 million in 2022. - As of a recent fiscal year, approximately $12.5 billion of the $15.0 billion share repurchase authorization remained available. pipeline_tag: sentence-similarity model-index: - name: BGE based finetuned on Domain results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 768 type: dim_768 metrics: - type: cosine_accuracy@1 value: 0.7042857142857143 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8328571428571429 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8728571428571429 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9185714285714286 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7042857142857143 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2776190476190476 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17457142857142854 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09185714285714283 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7042857142857143 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8328571428571429 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8728571428571429 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9185714285714286 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.812401187613736 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7784172335600903 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7815095527802808 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.7014285714285714 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8328571428571429 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.87 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9142857142857143 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7014285714285714 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2776190476190476 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.174 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09142857142857141 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7014285714285714 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8328571428571429 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.87 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9142857142857143 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.809056064041375 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.775240362811791 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7786994072067401 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.7042857142857143 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8228571428571428 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.87 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9128571428571428 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7042857142857143 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2742857142857143 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.174 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09128571428571428 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7042857142857143 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8228571428571428 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.87 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9128571428571428 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.80842418168086 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7750958049886617 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7786073403809471 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.68 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8185714285714286 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8514285714285714 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9057142857142857 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.68 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27285714285714285 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17028571428571426 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09057142857142855 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.68 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8185714285714286 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8514285714285714 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9057142857142857 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7928737154031139 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7568611111111109 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.760752382280591 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.6685714285714286 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7914285714285715 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8257142857142857 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8771428571428571 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6685714285714286 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2638095238095238 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16514285714285712 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.0877142857142857 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6685714285714286 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7914285714285715 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8257142857142857 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8771428571428571 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7719584095167248 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7385481859410428 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7436098705616472 name: Cosine Map@100 --- # BGE based finetuned on Domain 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:** cc-by-nc-sa-4.0 ### 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("tlphams/test_bge_finetuned_v0.1") # Run inference sentences = [ 'On which pages does the Glossary of Terms and Acronyms appear in the financial document?', "The 'Glossary of Terms and Acronyms' is included on pages 315-321 in the financial document.", 'Total operating expenses for the fiscal year ended January 31 were $21,962 million in 2023 and $18,918 million in 2022.', ] 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 | 0.7043 | | cosine_accuracy@3 | 0.8329 | | cosine_accuracy@5 | 0.8729 | | cosine_accuracy@10 | 0.9186 | | cosine_precision@1 | 0.7043 | | cosine_precision@3 | 0.2776 | | cosine_precision@5 | 0.1746 | | cosine_precision@10 | 0.0919 | | cosine_recall@1 | 0.7043 | | cosine_recall@3 | 0.8329 | | cosine_recall@5 | 0.8729 | | cosine_recall@10 | 0.9186 | | cosine_ndcg@10 | 0.8124 | | cosine_mrr@10 | 0.7784 | | **cosine_map@100** | **0.7815** | #### 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 | 0.7014 | | cosine_accuracy@3 | 0.8329 | | cosine_accuracy@5 | 0.87 | | cosine_accuracy@10 | 0.9143 | | cosine_precision@1 | 0.7014 | | cosine_precision@3 | 0.2776 | | cosine_precision@5 | 0.174 | | cosine_precision@10 | 0.0914 | | cosine_recall@1 | 0.7014 | | cosine_recall@3 | 0.8329 | | cosine_recall@5 | 0.87 | | cosine_recall@10 | 0.9143 | | cosine_ndcg@10 | 0.8091 | | cosine_mrr@10 | 0.7752 | | **cosine_map@100** | **0.7787** | #### 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 | 0.7043 | | cosine_accuracy@3 | 0.8229 | | cosine_accuracy@5 | 0.87 | | cosine_accuracy@10 | 0.9129 | | cosine_precision@1 | 0.7043 | | cosine_precision@3 | 0.2743 | | cosine_precision@5 | 0.174 | | cosine_precision@10 | 0.0913 | | cosine_recall@1 | 0.7043 | | cosine_recall@3 | 0.8229 | | cosine_recall@5 | 0.87 | | cosine_recall@10 | 0.9129 | | cosine_ndcg@10 | 0.8084 | | cosine_mrr@10 | 0.7751 | | **cosine_map@100** | **0.7786** | #### 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 | 0.68 | | cosine_accuracy@3 | 0.8186 | | cosine_accuracy@5 | 0.8514 | | cosine_accuracy@10 | 0.9057 | | cosine_precision@1 | 0.68 | | cosine_precision@3 | 0.2729 | | cosine_precision@5 | 0.1703 | | cosine_precision@10 | 0.0906 | | cosine_recall@1 | 0.68 | | cosine_recall@3 | 0.8186 | | cosine_recall@5 | 0.8514 | | cosine_recall@10 | 0.9057 | | cosine_ndcg@10 | 0.7929 | | cosine_mrr@10 | 0.7569 | | **cosine_map@100** | **0.7608** | #### 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 | 0.6686 | | cosine_accuracy@3 | 0.7914 | | cosine_accuracy@5 | 0.8257 | | cosine_accuracy@10 | 0.8771 | | cosine_precision@1 | 0.6686 | | cosine_precision@3 | 0.2638 | | cosine_precision@5 | 0.1651 | | cosine_precision@10 | 0.0877 | | cosine_recall@1 | 0.6686 | | cosine_recall@3 | 0.7914 | | cosine_recall@5 | 0.8257 | | cosine_recall@10 | 0.8771 | | cosine_ndcg@10 | 0.772 | | cosine_mrr@10 | 0.7385 | | **cosine_map@100** | **0.7436** | ## Training Details ### Training Dataset #### Unnamed Dataset * 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 | |:--------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | What were the changes in cash flow from investing activities for the fiscal years 2023 and 2022, and what drove these changes? | The cash flow from investing activities experienced significant changes between 2023 and 2022, influenced by the net changes in short-term investments, which shifted from an outflow to an inflow. | | How much did the stock-based compensation expenses change in 2023 compared to 2022? | Stock-based compensation expenses decreased by $88.9 million, or 16%, for the year ended December 31, 2023 compared to 2022. | | How does Credit Karma support its financial services? | To provide these services to its members, Credit Karma works with a variety of partners, including credit bureaus, banks, credit card issuers, insurance carriers, and other financial institutions and lending partners. | * 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 | 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 | |:----------:|:------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| | 0.9746 | 12 | 0.7475 | 0.7654 | 0.7693 | 0.7059 | 0.7741 | | 1.9492 | 24 | 0.7548 | 0.7733 | 0.7770 | 0.7325 | 0.7761 | | 2.9239 | 36 | 0.7599 | 0.7784 | 0.7782 | 0.7429 | 0.7818 | | **3.8985** | **48** | **0.7608** | **0.7786** | **0.7787** | **0.7436** | **0.7815** | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.12.3 - Sentence Transformers: 3.0.1 - Transformers: 4.41.2 - PyTorch: 2.3.1+cu121 - Accelerate: 0.31.0 - Datasets: 2.20.0 - 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} } ```