--- 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: The table indicates that 18,000 deferred shares were granted to non-employee directors in fiscal 2020, 15,000 in fiscal 2021, and 19,000 in fiscal 2022. sentences: - What was the primary reason for the increased audit effort for PCC goodwill and indefinite-lived intangible assets? - How many deferred shares were granted to non-employee directors in fiscal 2020, 2021, and 2022? - What was the total intrinsic value of options exercised in fiscal year 2023? - source_sentence: In Resource Masking Industries, we expect the profit impact from lower sales volume to be partially offset by favorable price realization. sentences: - By what percentage did Electronic Arts' operating income grow in the fiscal year ended March 31, 2023? - What impact is expected on Resource Industries' profit due to lower sales volume? - How are IBM’s 2023 Annual Report to Stockholders' financial statements made a part of Form 10-K? - source_sentence: The actuarial gain during the year ended December 31, 2022 was primarily related to increases in the discount rate assumptions. sentences: - What was the primary reason for the actuarial gain during the year ended December 31, 2022? - How much did Ford's total assets amount to by December 31, 2023? - What was the remaining available amount of the share repurchase authorization as of January 29, 2023? - source_sentence: Returned $1.7 billion to shareholders through share repurchases and dividend payments. sentences: - What was the carrying amount of investments without readily determinable fair values as of December 31, 2023? - What are the significant inputs to the valuation of Goldman Sachs' unsecured short- and long-term borrowings? - How much did the company return to shareholders through share repurchases and dividend payments in 2022? - source_sentence: The remaining amount available for borrowing under the Revolving Credit Facility as of December 31, 2023, was $2,245.2 million. sentences: - What was the total amount available for borrowing under the Revolving Credit Facility at Iron Mountain as of December 31, 2023? - What type of information is included in Note 13 of the Annual Report on Form 10-K? - How much did local currency revenue increase in Latin America in 2023 compared to 2022? 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: 0.6828571428571428 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8242857142857143 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8557142857142858 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9057142857142857 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6828571428571428 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2747619047619047 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17114285714285712 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09057142857142855 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6828571428571428 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8242857142857143 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8557142857142858 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9057142857142857 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7963610970343802 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7612930839002267 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7648513048205645 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: 0.8157142857142857 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8542857142857143 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.68 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27190476190476187 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17085714285714285 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.68 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8157142857142857 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8542857142857143 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7911616934987842 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7562284580498863 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.760087172570928 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.68 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8114285714285714 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8485714285714285 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8971428571428571 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.68 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2704761904761905 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16971428571428568 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.0897142857142857 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.68 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8114285714285714 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8485714285714285 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8971428571428571 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7888581850866868 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7542278911564625 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7579536807505182 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.6571428571428571 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.79 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8285714285714286 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8857142857142857 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6571428571428571 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2633333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1657142857142857 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08857142857142856 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6571428571428571 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.79 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8285714285714286 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8857142857142857 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7703812626851927 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.733632653061224 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7378840513025602 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.62 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.77 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8028571428571428 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.85 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.62 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.25666666666666665 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16057142857142856 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.085 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.62 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.77 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8028571428571428 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.85 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.73777886683529 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7016190476190474 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7073607864232172 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("moritzglnr/bge-base-financial-matryoshka") # Run inference sentences = [ 'The remaining amount available for borrowing under the Revolving Credit Facility as of December 31, 2023, was $2,245.2 million.', 'What was the total amount available for borrowing under the Revolving Credit Facility at Iron Mountain as of December 31, 2023?', 'What type of information is included in Note 13 of the Annual Report on Form 10-K?', ] 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 | 0.6829 | | cosine_accuracy@3 | 0.8243 | | cosine_accuracy@5 | 0.8557 | | cosine_accuracy@10 | 0.9057 | | cosine_precision@1 | 0.6829 | | cosine_precision@3 | 0.2748 | | cosine_precision@5 | 0.1711 | | cosine_precision@10 | 0.0906 | | cosine_recall@1 | 0.6829 | | cosine_recall@3 | 0.8243 | | cosine_recall@5 | 0.8557 | | cosine_recall@10 | 0.9057 | | cosine_ndcg@10 | 0.7964 | | cosine_mrr@10 | 0.7613 | | **cosine_map@100** | **0.7649** | #### 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 | 0.8157 | | cosine_accuracy@5 | 0.8543 | | cosine_accuracy@10 | 0.9 | | cosine_precision@1 | 0.68 | | cosine_precision@3 | 0.2719 | | cosine_precision@5 | 0.1709 | | cosine_precision@10 | 0.09 | | cosine_recall@1 | 0.68 | | cosine_recall@3 | 0.8157 | | cosine_recall@5 | 0.8543 | | cosine_recall@10 | 0.9 | | cosine_ndcg@10 | 0.7912 | | cosine_mrr@10 | 0.7562 | | **cosine_map@100** | **0.7601** | #### 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 | 0.68 | | cosine_accuracy@3 | 0.8114 | | cosine_accuracy@5 | 0.8486 | | cosine_accuracy@10 | 0.8971 | | cosine_precision@1 | 0.68 | | cosine_precision@3 | 0.2705 | | cosine_precision@5 | 0.1697 | | cosine_precision@10 | 0.0897 | | cosine_recall@1 | 0.68 | | cosine_recall@3 | 0.8114 | | cosine_recall@5 | 0.8486 | | cosine_recall@10 | 0.8971 | | cosine_ndcg@10 | 0.7889 | | cosine_mrr@10 | 0.7542 | | **cosine_map@100** | **0.758** | #### 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 | 0.6571 | | cosine_accuracy@3 | 0.79 | | cosine_accuracy@5 | 0.8286 | | cosine_accuracy@10 | 0.8857 | | cosine_precision@1 | 0.6571 | | cosine_precision@3 | 0.2633 | | cosine_precision@5 | 0.1657 | | cosine_precision@10 | 0.0886 | | cosine_recall@1 | 0.6571 | | cosine_recall@3 | 0.79 | | cosine_recall@5 | 0.8286 | | cosine_recall@10 | 0.8857 | | cosine_ndcg@10 | 0.7704 | | cosine_mrr@10 | 0.7336 | | **cosine_map@100** | **0.7379** | #### 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 | 0.62 | | cosine_accuracy@3 | 0.77 | | cosine_accuracy@5 | 0.8029 | | cosine_accuracy@10 | 0.85 | | cosine_precision@1 | 0.62 | | cosine_precision@3 | 0.2567 | | cosine_precision@5 | 0.1606 | | cosine_precision@10 | 0.085 | | cosine_recall@1 | 0.62 | | cosine_recall@3 | 0.77 | | cosine_recall@5 | 0.8029 | | cosine_recall@10 | 0.85 | | cosine_ndcg@10 | 0.7378 | | cosine_mrr@10 | 0.7016 | | **cosine_map@100** | **0.7074** | ## 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 | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------| | We utilize a full yield curve approach in the estimation of service and interest costs by applying the specific spot rates along the yield curve used in the determination of the benefit obligation to the relevant projected cash flows. This approach provides a more precise measurement of service and interest costs by improving the correlation between the projected cash flows to the corresponding spot rates along the yield curve. This approach does not affect the measurement of our pension and other post-retirement benefit liabilities but generally results in lower benefit expense in periods when the yield curve is upward sloping. | How does the use of a full yield curve approach in estimating pension costs affect the measurement of liabilities and expenses? | | Ending | 8,134 | | 8,206 | | 16,340 | | 8,061 | | 8,016 | 16,077 | What was the ending store count for the Family Dollar segment after the fiscal year ended January 28, 2023? | | The company's capital expenditures for 2024 are expected to be approximately $5.7 billion. | How much does the company expect to spend on capital expenditures in 2024? | * 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 | 1.5661 | - | - | - | - | - | | 0.9746 | 12 | - | 0.7151 | 0.7378 | 0.7443 | 0.6680 | 0.7546 | | 1.6244 | 20 | 0.6602 | - | - | - | - | - | | 1.9492 | 24 | - | 0.7326 | 0.7533 | 0.7564 | 0.7037 | 0.7640 | | 2.4365 | 30 | 0.4675 | - | - | - | - | - | | 2.9239 | 36 | - | 0.7384 | 0.7575 | 0.7601 | 0.7086 | 0.7643 | | 3.2487 | 40 | 0.3891 | - | - | - | - | - | | **3.8985** | **48** | **-** | **0.7379** | **0.758** | **0.7601** | **0.7074** | **0.7649** | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.1 - Transformers: 4.41.2 - PyTorch: 2.1.2+cu121 - Accelerate: 0.32.1 - 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} } ```