--- 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:9600 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: The median home value in San Carlos, CA is $2,350,000. sentences: - What does the console property of the WorkerGlobalScope interface provide access to? - What is the last sold price and date for the property at 4372 W 14th Street Dr, Greeley, CO 80634? - What is the median home value in San Carlos, CA? - source_sentence: The four new principals hired by Superintendent of Schools Ken Kenworthy for the Okeechobee school system are Joseph Stanley at Central Elementary, Jody Hays at Yearling Middle School, Tuuli Robinson at North Elementary, and Dr. Thelma Jackson at Seminole Elementary School. sentences: - Who won the gold medal in the men's 1,500m final at the speed skating World Cup? - What is the purpose of the 1,2,3 bowling activity for toddlers? - Who are the four new principals hired by Superintendent of Schools Ken Kenworthy for the Okeechobee school system? - source_sentence: Twitter Audit is used to scan your followers and find out what percentage of them are real people. sentences: - What is the main product discussed in the context of fair trade? - What is the software mentioned in the context suitable for? - What is the purpose of the Twitter Audit tool? - source_sentence: Michael Czysz made the 2011 E1pc lighter and more powerful than the 2010 version, and also improved the software controlling the bike’s D1g1tal powertrain. sentences: - What changes did Michael Czysz make to the 2011 E1pc compared to the 2010 version? - What is the author's suggestion for leaving a legacy for future generations? - What is the most affordable and reliable option to fix a MacBook according to the technician? - source_sentence: HTC called the Samsung Galaxy S4 “mainstream”. sentences: - What is the essential aspect of the vocation to marriage according to Benedict XVI's message on the 40th Anniversary of Humanae Vitae? - What did HTC announce about the Samsung Galaxy S4? - What was Allan Cox's First Class Delivery launched on for his Level 1 certification flight? 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: 0.9675 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9791666666666666 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9829166666666667 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.98875 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.9675 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3263888888888889 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1965833333333333 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09887499999999999 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.9675 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.9791666666666666 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9829166666666667 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.98875 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9776735843960416 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.9741727843915341 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.974471752833939 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.9641666666666666 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9775 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9816666666666667 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.98875 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.9641666666666666 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3258333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1963333333333333 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09887499999999999 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.9641666666666666 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.9775 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9816666666666667 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.98875 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9758504869144781 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.9717977843915344 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.9720465527215371 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.9620833333333333 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9741666666666666 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9804166666666667 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.98625 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.9620833333333333 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.32472222222222225 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1960833333333333 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09862499999999999 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.9620833333333333 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.9741666666666666 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9804166666666667 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.98625 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9737941784937224 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.9698406084656085 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.9702070899963996 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.9554166666666667 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.97 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9766666666666667 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.98375 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.9554166666666667 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3233333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1953333333333333 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09837499999999999 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.9554166666666667 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.97 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9766666666666667 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.98375 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.969307497603498 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.9647410714285715 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.9652034022263717 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.9391666666666667 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9616666666666667 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9666666666666667 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9758333333333333 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.9391666666666667 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3205555555555556 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1933333333333333 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09758333333333333 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.9391666666666667 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.9616666666666667 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9666666666666667 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9758333333333333 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9577277779716886 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.9519417989417989 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.9525399354798056 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("juanpablomesa/bge-base-financial-matryoshka") # Run inference sentences = [ 'HTC called the Samsung Galaxy S4 “mainstream”.', 'What did HTC announce about the Samsung Galaxy S4?', "What is the essential aspect of the vocation to marriage according to Benedict XVI's message on the 40th Anniversary of Humanae Vitae?", ] 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 | 0.9675 | | cosine_accuracy@3 | 0.9792 | | cosine_accuracy@5 | 0.9829 | | cosine_accuracy@10 | 0.9888 | | cosine_precision@1 | 0.9675 | | cosine_precision@3 | 0.3264 | | cosine_precision@5 | 0.1966 | | cosine_precision@10 | 0.0989 | | cosine_recall@1 | 0.9675 | | cosine_recall@3 | 0.9792 | | cosine_recall@5 | 0.9829 | | cosine_recall@10 | 0.9888 | | cosine_ndcg@10 | 0.9777 | | cosine_mrr@10 | 0.9742 | | **cosine_map@100** | **0.9745** | #### 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 | 0.9642 | | cosine_accuracy@3 | 0.9775 | | cosine_accuracy@5 | 0.9817 | | cosine_accuracy@10 | 0.9888 | | cosine_precision@1 | 0.9642 | | cosine_precision@3 | 0.3258 | | cosine_precision@5 | 0.1963 | | cosine_precision@10 | 0.0989 | | cosine_recall@1 | 0.9642 | | cosine_recall@3 | 0.9775 | | cosine_recall@5 | 0.9817 | | cosine_recall@10 | 0.9888 | | cosine_ndcg@10 | 0.9759 | | cosine_mrr@10 | 0.9718 | | **cosine_map@100** | **0.972** | #### 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 | 0.9621 | | cosine_accuracy@3 | 0.9742 | | cosine_accuracy@5 | 0.9804 | | cosine_accuracy@10 | 0.9862 | | cosine_precision@1 | 0.9621 | | cosine_precision@3 | 0.3247 | | cosine_precision@5 | 0.1961 | | cosine_precision@10 | 0.0986 | | cosine_recall@1 | 0.9621 | | cosine_recall@3 | 0.9742 | | cosine_recall@5 | 0.9804 | | cosine_recall@10 | 0.9862 | | cosine_ndcg@10 | 0.9738 | | cosine_mrr@10 | 0.9698 | | **cosine_map@100** | **0.9702** | #### 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 | 0.9554 | | cosine_accuracy@3 | 0.97 | | cosine_accuracy@5 | 0.9767 | | cosine_accuracy@10 | 0.9838 | | cosine_precision@1 | 0.9554 | | cosine_precision@3 | 0.3233 | | cosine_precision@5 | 0.1953 | | cosine_precision@10 | 0.0984 | | cosine_recall@1 | 0.9554 | | cosine_recall@3 | 0.97 | | cosine_recall@5 | 0.9767 | | cosine_recall@10 | 0.9838 | | cosine_ndcg@10 | 0.9693 | | cosine_mrr@10 | 0.9647 | | **cosine_map@100** | **0.9652** | #### 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 | 0.9392 | | cosine_accuracy@3 | 0.9617 | | cosine_accuracy@5 | 0.9667 | | cosine_accuracy@10 | 0.9758 | | cosine_precision@1 | 0.9392 | | cosine_precision@3 | 0.3206 | | cosine_precision@5 | 0.1933 | | cosine_precision@10 | 0.0976 | | cosine_recall@1 | 0.9392 | | cosine_recall@3 | 0.9617 | | cosine_recall@5 | 0.9667 | | cosine_recall@10 | 0.9758 | | cosine_ndcg@10 | 0.9577 | | cosine_mrr@10 | 0.9519 | | **cosine_map@100** | **0.9525** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 9,600 training samples * Columns: positive and anchor * Approximate statistics based on the first 1000 samples: | | positive | anchor | |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | positive | anchor | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------| | The Berry Export Summary 2028 is a dedicated export plan for the Australian strawberry, raspberry, and blackberry industries. It maps the sectors’ current position, where they want to be, high-opportunity markets, and next steps. The purpose of this plan is to grow their global presence over the next 10 years. | What is the Berry Export Summary 2028 and what is its purpose? | | Benefits reported from having access to Self-supply water sources include convenience, less time spent for fetching water and access to more and better quality water. In some areas, Self-supply sources offer important added values such as water for productive use, income generation, family safety and improved food security. | What are some of the benefits reported from having access to Self-supply water sources? | | The unique features of the Coolands for Twitter app include Real-Time updates without the need for a refresh button, Avatar Indicator which shows small avatars on the title bar for new messages, Direct Link for intuitive and convenient link opening, Smart Bookmark to easily return to previous reading position, and User Level Notification which allows customized notification settings for different users. | What are the unique features of the Coolands for Twitter app? | * 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 | |:--------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| | 0.5333 | 10 | 0.6065 | - | - | - | - | - | | 0.96 | 18 | - | 0.9583 | 0.9674 | 0.9695 | 0.9372 | 0.9708 | | 1.0667 | 20 | 0.3313 | - | - | - | - | - | | 1.6 | 30 | 0.144 | - | - | - | - | - | | 1.9733 | 37 | - | 0.9630 | 0.9699 | 0.9716 | 0.9488 | 0.9745 | | 2.1333 | 40 | 0.1317 | - | - | - | - | - | | 2.6667 | 50 | 0.0749 | - | - | - | - | - | | 2.9867 | 56 | - | 0.9650 | 0.9701 | 0.9721 | 0.9522 | 0.9747 | | 3.2 | 60 | 0.088 | - | - | - | - | - | | 3.7333 | 70 | 0.0598 | - | - | - | - | - | | **3.84** | **72** | **-** | **0.9652** | **0.9702** | **0.972** | **0.9525** | **0.9745** | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.11.5 - 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} } ```