--- 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: A number of factors may impact ESKD growth rates, including mortality rates for dialysis patients or CKD patients, the aging of the U.S. population, transplant rates, incidence rates for diseases that cause kidney failure such as diabetes and hypertension, growth rates of minority populations with higher than average incidence rates of ESKD. sentences: - By how much did the company increase its quarterly cash dividend in February 2023? - What factors may impact the growth rates of the ESKD patient population? - What percentage increase did salaries and related costs experience at Delta Air Lines from 2022 to 2023? - source_sentence: HIV product sales increased 6% to $18.2 billion in 2023, compared to 2022. sentences: - What were the present values of lease liabilities for operating and finance leases as of December 31, 2023? - By what percentage did HIV product sales increase in 2023 compared to the previous year? - How is interest income not attributable to the Card Member loan portfolio primarily represented in financial documents? - source_sentence: If a violation is found, a broad range of remedies is potentially available to the Commission and/or CMA, including imposing a fine and/or the prohibition or restriction of certain business practices. sentences: - What are the potential remedies if a violation is found by the European Commission or the U.K. Competition and Markets Authority in their investigation of automotive companies? - By which auditing standards were the consolidated financial statements of Salesforce, Inc. audited? - What is the main role of Kroger's Chief Executive Officer in the company? - source_sentence: The discussion in Hewlett Packard Enterprise's Form 10-K highlights factors impacting costs and revenues, including easing supply chain constraints, foreign exchange pressures, inflationary trends, and recent tax developments potentially affecting their financial outcomes. sentences: - Is the outcome of the investigation into Tesla's waste segregation practices currently determinable? - How does Hewlett Packard Enterprise justify the exclusion of transformation costs from its non-GAAP financial measures? - In the context of Hewlett Packard Enterprise's recent financial discussions, what factors are expected to impact their operational costs and revenue growth moving forward? - source_sentence: Our Records Management and Data Management service revenue growth is being negatively impacted by declining activity rates as stored records and tapes are becoming less active and more archival. sentences: - How is Iron Mountain addressing the decline in activity rates in their Records and Data Management services? - What services do companies that build fiber-based networks provide in the Connectivity & Platforms markets? - What business outcomes is HPE focused on accelerating with its technological solutions? 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: 0.7057142857142857 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8457142857142858 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8785714285714286 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9114285714285715 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7057142857142857 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2819047619047619 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17571428571428568 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09114285714285714 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7057142857142857 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8457142857142858 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8785714285714286 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9114285714285715 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8125296344519609 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7804263038548749 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7839408125709297 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.7071428571428572 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8428571428571429 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8742857142857143 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9114285714285715 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7071428571428572 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.28095238095238095 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17485714285714282 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09114285714285714 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7071428571428572 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8428571428571429 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8742857142857143 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9114285714285715 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8126517351231356 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7807267573696143 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7841188299664252 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.7028571428571428 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8357142857142857 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8685714285714285 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9071428571428571 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7028571428571428 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2785714285714286 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1737142857142857 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09071428571428572 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7028571428571428 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8357142857142857 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8685714285714285 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9071428571428571 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8086618947757659 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7768820861678005 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7806177775944575 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: 0.82 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8557142857142858 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9014285714285715 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6914285714285714 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2733333333333334 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17114285714285712 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09014285714285714 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6914285714285714 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.82 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8557142857142858 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9014285714285715 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7980982703041672 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7650045351473919 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7688564414027702 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.6542857142857142 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7885714285714286 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8328571428571429 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8828571428571429 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6542857142857142 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.26285714285714284 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16657142857142856 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08828571428571427 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6542857142857142 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7885714285714286 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8328571428571429 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8828571428571429 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7689665884678363 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7325351473922898 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7369423610264151 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("NickyNicky/bge-base-financial-matryoshka") # Run inference sentences = [ 'Our Records Management and Data Management service revenue growth is being negatively impacted by declining activity rates as stored records and tapes are becoming less active and more archival.', 'How is Iron Mountain addressing the decline in activity rates in their Records and Data Management services?', 'What services do companies that build fiber-based networks provide in the Connectivity & Platforms markets?', ] 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 | 0.7057 | | cosine_accuracy@3 | 0.8457 | | cosine_accuracy@5 | 0.8786 | | cosine_accuracy@10 | 0.9114 | | cosine_precision@1 | 0.7057 | | cosine_precision@3 | 0.2819 | | cosine_precision@5 | 0.1757 | | cosine_precision@10 | 0.0911 | | cosine_recall@1 | 0.7057 | | cosine_recall@3 | 0.8457 | | cosine_recall@5 | 0.8786 | | cosine_recall@10 | 0.9114 | | cosine_ndcg@10 | 0.8125 | | cosine_mrr@10 | 0.7804 | | **cosine_map@100** | **0.7839** | #### 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 | 0.7071 | | cosine_accuracy@3 | 0.8429 | | cosine_accuracy@5 | 0.8743 | | cosine_accuracy@10 | 0.9114 | | cosine_precision@1 | 0.7071 | | cosine_precision@3 | 0.281 | | cosine_precision@5 | 0.1749 | | cosine_precision@10 | 0.0911 | | cosine_recall@1 | 0.7071 | | cosine_recall@3 | 0.8429 | | cosine_recall@5 | 0.8743 | | cosine_recall@10 | 0.9114 | | cosine_ndcg@10 | 0.8127 | | cosine_mrr@10 | 0.7807 | | **cosine_map@100** | **0.7841** | #### 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 | 0.7029 | | cosine_accuracy@3 | 0.8357 | | cosine_accuracy@5 | 0.8686 | | cosine_accuracy@10 | 0.9071 | | cosine_precision@1 | 0.7029 | | cosine_precision@3 | 0.2786 | | cosine_precision@5 | 0.1737 | | cosine_precision@10 | 0.0907 | | cosine_recall@1 | 0.7029 | | cosine_recall@3 | 0.8357 | | cosine_recall@5 | 0.8686 | | cosine_recall@10 | 0.9071 | | cosine_ndcg@10 | 0.8087 | | cosine_mrr@10 | 0.7769 | | **cosine_map@100** | **0.7806** | #### 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 | 0.82 | | cosine_accuracy@5 | 0.8557 | | cosine_accuracy@10 | 0.9014 | | cosine_precision@1 | 0.6914 | | cosine_precision@3 | 0.2733 | | cosine_precision@5 | 0.1711 | | cosine_precision@10 | 0.0901 | | cosine_recall@1 | 0.6914 | | cosine_recall@3 | 0.82 | | cosine_recall@5 | 0.8557 | | cosine_recall@10 | 0.9014 | | cosine_ndcg@10 | 0.7981 | | cosine_mrr@10 | 0.765 | | **cosine_map@100** | **0.7689** | #### 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 | 0.6543 | | cosine_accuracy@3 | 0.7886 | | cosine_accuracy@5 | 0.8329 | | cosine_accuracy@10 | 0.8829 | | cosine_precision@1 | 0.6543 | | cosine_precision@3 | 0.2629 | | cosine_precision@5 | 0.1666 | | cosine_precision@10 | 0.0883 | | cosine_recall@1 | 0.6543 | | cosine_recall@3 | 0.7886 | | cosine_recall@5 | 0.8329 | | cosine_recall@10 | 0.8829 | | cosine_ndcg@10 | 0.769 | | cosine_mrr@10 | 0.7325 | | **cosine_map@100** | **0.7369** | ## 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 | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------| | Internationally, Visa Inc.'s commercial payments volume grew by 23% from $407 billion in 2021 to $500 billion in 2022. | What was the growth rate of Visa Inc.'s commercial payments volume internationally between 2021 and 2022? | | The consolidated financial statements and accompanying notes listed in Part IV, Item 15(a)(1) of this Annual Report on Form 10-K are included immediately following Part IV hereof. | Where can one find the consolidated financial statements and accompanying notes in the Annual Report on Form 10-K? | | The additional paid-in capital at the end of 2023 was recorded as $114,519 million. | What was the amount recorded for additional paid-in capital at the end of 2023? | * 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`: 80 - `per_device_eval_batch_size`: 16 - `gradient_accumulation_steps`: 16 - `learning_rate`: 2e-05 - `num_train_epochs`: 15 - `lr_scheduler_type`: cosine - `warmup_ratio`: 0.1 - `bf16`: True - `tf32`: 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`: 80 - `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`: 15 - `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`: False - `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 | |:-------:|:----:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| | 0.8101 | 4 | - | 0.7066 | 0.7309 | 0.7390 | 0.6462 | 0.7441 | | 1.8228 | 9 | - | 0.7394 | 0.7497 | 0.7630 | 0.6922 | 0.7650 | | 2.0253 | 10 | 2.768 | - | - | - | - | - | | 2.8354 | 14 | - | 0.7502 | 0.7625 | 0.7767 | 0.7208 | 0.7787 | | 3.8481 | 19 | - | 0.7553 | 0.7714 | 0.7804 | 0.7234 | 0.7802 | | 4.0506 | 20 | 1.1294 | - | - | - | - | - | | 4.8608 | 24 | - | 0.7577 | 0.7769 | 0.7831 | 0.7327 | 0.7858 | | 5.8734 | 29 | - | 0.7616 | 0.7775 | 0.7832 | 0.7335 | 0.7876 | | 6.0759 | 30 | 0.7536 | - | - | - | - | - | | 6.8861 | 34 | - | 0.7624 | 0.7788 | 0.7832 | 0.7352 | 0.7882 | | 7.8987 | 39 | - | 0.7665 | 0.7795 | 0.7814 | 0.7359 | 0.7861 | | 8.1013 | 40 | 0.5846 | - | - | - | - | - | | 8.9114 | 44 | - | 0.7688 | 0.7801 | 0.7828 | 0.7360 | 0.7857 | | 9.9241 | 49 | - | 0.7698 | 0.7804 | 0.7836 | 0.7367 | 0.7840 | | 10.1266 | 50 | 0.5187 | - | - | - | - | - | | 10.9367 | 54 | - | 0.7692 | 0.7801 | 0.7827 | 0.7383 | 0.7837 | | 11.9494 | 59 | - | 0.7698 | 0.7801 | 0.7834 | 0.7377 | 0.7849 | | 12.1519 | 60 | 0.4949 | 0.7689 | 0.7806 | 0.7841 | 0.7369 | 0.7839 | ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.1 - Transformers: 4.41.2 - PyTorch: 2.2.0+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} } ```