--- language: - en license: apache-2.0 tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:6300 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss base_model: BAAI/bge-base-en-v1.5 widget: - source_sentence: The net cash provided by operating activities during fiscal 2023 was related to net income of $208 million, adjusted for non-cash items including $3.8 billion of depreciation and amortization and $3.3 billion related to stock-based compensation expense. sentences: - What are the three key aspects encompassed in a company's internal control over financial reporting? - What was the net cash provided by operating activities for fiscal 2023? - What are the two operating segments of NVIDIA as mentioned in the text? - source_sentence: Intellectual Property To establish and protect our proprietary rights, we rely on a combination of patents, trademarks, copyrights, trade secrets, including know-how, license agreements, confidentiality procedures, non-disclosure agreements with third parties, employee disclosure and invention assignment agreements, and other contractual rights. sentences: - What condition does Synthroid treat and what type of drug is it formulated as? - What legal tools does the company use to protect its intellectual property? - In which item and part of a financial document would you find information on legal proceedings? - source_sentence: Cost of revenues is comprised of TAC and other costs of revenues. TAC includes amounts paid to our distribution partners and Google Network partners primarily for ads displayed on their properties. Other cost of revenues includes compensation expense related to our data centers and operations, content acquisition costs, depreciation expense related to technical infrastructure, and inventory and other costs related to devices we sell. sentences: - What is included in the cost of revenues for Google? - What was the total net uncertain tax positions as of December 31, 2023? - What portion of the restructuring charges incurred in fiscal 2023 are expected to be settled with cash? - source_sentence: Comprehensive income (loss) | $ | (362) | | $ | 1,868 | $ | 4,775 sentences: - What measures does the company take to ensure product quality? - How many pages does Item 8, which includes Financial Statements and Supplementary Data, span? - What was the total comprehensive income for Airbnb, Inc. in 2023? - source_sentence: We make our branded beverage products available to consumers throughout the world through our network of independent bottling partners, distributors, wholesalers and retailers as well as our consolidated bottling and distribution operations. sentences: - How does The Coca-Cola Company distribute its beverage products globally? - What accounting method is predominantly used to determine inventory costs in the Company's supermarket divisions before LIFO adjustments? - How are the company's inventories valued? pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 model-index: - name: 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: 0.7142857142857143 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8485714285714285 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8814285714285715 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9171428571428571 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7142857142857143 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.28285714285714286 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17628571428571424 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09171428571428569 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7142857142857143 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8485714285714285 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8814285714285715 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9171428571428571 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8195547708074192 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7879784580498865 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.791495828863575 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.7157142857142857 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8457142857142858 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8814285714285715 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.92 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7157142857142857 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2819047619047619 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17628571428571424 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09199999999999998 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7157142857142857 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8457142857142858 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8814285714285715 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.92 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8200080507124731 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7878299319727888 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7911645774121049 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.6914285714285714 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8471428571428572 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.88 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.91 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6914285714285714 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.28238095238095234 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.176 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09099999999999998 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6914285714285714 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8471428571428572 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.88 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.91 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8087696033003087 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7755997732426303 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7799208675704249 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.6914285714285714 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.83 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.87 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9071428571428571 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6914285714285714 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27666666666666667 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.174 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.0907142857142857 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6914285714285714 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.83 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.87 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9071428571428571 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8024684596621504 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7686116780045347 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7729258054107728 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.6585714285714286 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8028571428571428 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8357142857142857 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8828571428571429 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6585714285714286 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2676190476190476 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1671428571428571 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08828571428571429 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6585714285714286 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8028571428571428 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8357142857142857 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8828571428571429 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7735846622621076 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.738378684807256 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7433829659777168 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) on the json dataset. 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 - **Training Dataset:** - json - **Language:** en - **License:** apache-2.0 ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': 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("girijesh/bge-base-financial-matryoshka") # Run inference sentences = [ 'We make our branded beverage products available to consumers throughout the world through our network of independent bottling partners, distributors, wholesalers and retailers as well as our consolidated bottling and distribution operations.', 'How does The Coca-Cola Company distribute its beverage products globally?', "What accounting method is predominantly used to determine inventory costs in the Company's supermarket divisions before LIFO adjustments?", ] 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 | 0.7143 | | cosine_accuracy@3 | 0.8486 | | cosine_accuracy@5 | 0.8814 | | cosine_accuracy@10 | 0.9171 | | cosine_precision@1 | 0.7143 | | cosine_precision@3 | 0.2829 | | cosine_precision@5 | 0.1763 | | cosine_precision@10 | 0.0917 | | cosine_recall@1 | 0.7143 | | cosine_recall@3 | 0.8486 | | cosine_recall@5 | 0.8814 | | cosine_recall@10 | 0.9171 | | cosine_ndcg@10 | 0.8196 | | cosine_mrr@10 | 0.788 | | **cosine_map@100** | **0.7915** | #### 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 | 0.7157 | | cosine_accuracy@3 | 0.8457 | | cosine_accuracy@5 | 0.8814 | | cosine_accuracy@10 | 0.92 | | cosine_precision@1 | 0.7157 | | cosine_precision@3 | 0.2819 | | cosine_precision@5 | 0.1763 | | cosine_precision@10 | 0.092 | | cosine_recall@1 | 0.7157 | | cosine_recall@3 | 0.8457 | | cosine_recall@5 | 0.8814 | | cosine_recall@10 | 0.92 | | cosine_ndcg@10 | 0.82 | | cosine_mrr@10 | 0.7878 | | **cosine_map@100** | **0.7912** | #### 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 | 0.6914 | | cosine_accuracy@3 | 0.8471 | | cosine_accuracy@5 | 0.88 | | cosine_accuracy@10 | 0.91 | | cosine_precision@1 | 0.6914 | | cosine_precision@3 | 0.2824 | | cosine_precision@5 | 0.176 | | cosine_precision@10 | 0.091 | | cosine_recall@1 | 0.6914 | | cosine_recall@3 | 0.8471 | | cosine_recall@5 | 0.88 | | cosine_recall@10 | 0.91 | | cosine_ndcg@10 | 0.8088 | | cosine_mrr@10 | 0.7756 | | **cosine_map@100** | **0.7799** | #### 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 | 0.6914 | | cosine_accuracy@3 | 0.83 | | cosine_accuracy@5 | 0.87 | | cosine_accuracy@10 | 0.9071 | | cosine_precision@1 | 0.6914 | | cosine_precision@3 | 0.2767 | | cosine_precision@5 | 0.174 | | cosine_precision@10 | 0.0907 | | cosine_recall@1 | 0.6914 | | cosine_recall@3 | 0.83 | | cosine_recall@5 | 0.87 | | cosine_recall@10 | 0.9071 | | cosine_ndcg@10 | 0.8025 | | cosine_mrr@10 | 0.7686 | | **cosine_map@100** | **0.7729** | #### 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 | 0.6586 | | cosine_accuracy@3 | 0.8029 | | cosine_accuracy@5 | 0.8357 | | cosine_accuracy@10 | 0.8829 | | cosine_precision@1 | 0.6586 | | cosine_precision@3 | 0.2676 | | cosine_precision@5 | 0.1671 | | cosine_precision@10 | 0.0883 | | cosine_recall@1 | 0.6586 | | cosine_recall@3 | 0.8029 | | cosine_recall@5 | 0.8357 | | cosine_recall@10 | 0.8829 | | cosine_ndcg@10 | 0.7736 | | cosine_mrr@10 | 0.7384 | | **cosine_map@100** | **0.7434** | ## Training Details ### Training Dataset #### json * Dataset: json * 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 | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------| | Change in control events potentially triggering benefits under the CIC Plan and Mr. Begor’s agreement would occur, subject to certain exceptions, if (1) any person acquires 20% or more of our voting stock; (2) upon a merger or other business combination, our shareholders receive less than two-thirds of the common stock and combined voting power of the new company; (3) members of the current Board of Directors ceasing to constitute a majority of the Board of Directors, except for new directors that are regularly elected; (4) we sell or otherwise dispose of all or substantially all of our assets; or (5) we liquidate or dissolve. | What events potentially trigger benefits under Mark W. Begor's change in control agreement and the CIC Plan? | | The growth in marketplace revenue was primarily due to the impact of the pricing update to increase our seller transaction fee for the Etsy marketplace from 5% to 6.5% beginning on April 11, 2022, and an increase in foreign currency payments, which we earn an additional transaction fee on, in the year ended December 31, 2023. | What drove the growth in marketplace revenue for the year ended December 31, 2023? | | We are focused on ensuring that we efficiently allocate our resources to the areas with the highest potential for profitable growth. ... The uncertain macroeconomic environment in many of these markets is expected to continue and we aim to ensure our investments in these international markets are appropriate relative to the size of the opportunity. | What are Hershey's goals for international expansion and how are they being approached? | * 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_768_cosine_map@100 | dim_512_cosine_map@100 | dim_256_cosine_map@100 | dim_128_cosine_map@100 | dim_64_cosine_map@100 | |:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:| | 0.9697 | 6 | - | 0.7527 | 0.7516 | 0.7454 | 0.7253 | 0.6808 | | 1.6162 | 10 | 2.3351 | - | - | - | - | - | | 1.9394 | 12 | - | 0.7740 | 0.7699 | 0.7707 | 0.7474 | 0.7188 | | 2.9091 | 18 | - | 0.7784 | 0.7790 | 0.7735 | 0.7575 | 0.7275 | | 3.2323 | 20 | 1.0519 | - | - | - | - | - | | **3.8788** | **24** | **-** | **0.7818** | **0.7784** | **0.7763** | **0.7581** | **0.7293** | | 0.9697 | 6 | - | 0.7836 | 0.7826 | 0.7817 | 0.7664 | 0.7353 | | 1.6162 | 10 | 0.8132 | - | - | - | - | - | | 1.9394 | 12 | - | 0.7887 | 0.7887 | 0.7837 | 0.7714 | 0.7409 | | 2.9091 | 18 | - | 0.7897 | 0.7902 | 0.7798 | 0.7721 | 0.7410 | | 3.2323 | 20 | 0.6098 | - | - | - | - | - | | **3.8788** | **24** | **-** | **0.7915** | **0.7912** | **0.7799** | **0.7729** | **0.7434** | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.2.1 - Transformers: 4.41.2 - PyTorch: 2.1.2+cu121 - Accelerate: 1.0.1 - 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} } ```