--- base_model: BAAI/bge-large-en-v1.5 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:1024 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: After rescue, survivors may require hospital treatment. This must be provided as quickly as possible. The SMC should consider having ambulance and hospital facilities ready. sentences: - What should the SMC consider having ready after a rescue? - What is critical for mass rescue operations? - What can computer programs do to relieve the search planner of computational burden? - source_sentence: SMCs conduct communication searches when facts are needed to supplement initially reported information. Efforts are continued to contact the craft, to find out more about a possible distress situation, and to prepare for or to avoid a search effort. Section 3.5 has more information on communication searches.MEDICO Communications sentences: - What is generally produced by dead-reckoning navigation alone for search aircraft? - What should be the widths of rectangular areas to be covered with a PS pattern and the lengths of rectangular areas to be covered with a CS pattern? - What is the purpose of SMCs conducting communication searches? - source_sentence: 'SAR facilities include designated SRUs and other resources which can be used to conduct or support SAR operations. An SRU is a unit composed of trained personnel and provided with equipment suitable for the expeditious and efficient conduct of search and rescue. An SRU can be an air, maritime, or land-based facility. Facilities selected as SRUs should be able to reach the scene of distress quickly and, in particular, be suitable for one or more of the following operations:– providing assistance to prevent or reduce the severity of accidents and the hardship of survivors, e.g., escorting an aircraft, standing by a sinking vessel;– conducting a search;– delivering supplies and survival equipment to the scene;– rescuing survivors;– providing food, medical or other initial needs of survivors; and– delivering the survivors to a place of safety. ' sentences: - What are the types of SAR facilities that can be used to conduct or support SAR operations? - What is the scenario in which a simulated communication search is carried out and an air search is planned? - What is discussed in detail in various other places in this Manual? - source_sentence: Support facilities enable the operational response resources (e.g., the RCC and SRUs) to provide the SAR services. Without the supporting resources, the operational resources cannot sustain effective operations. There is a wide range of support facilities and services, which include the following:Training facilities Facility maintenanceCommunications facilities Management functionsNavigation systems Research and developmentSAR data providers (SDPs) PlanningMedical facilities ExercisesAircraft landing fields Refuelling servicesVoluntary services (e.g., Red Cross) Critical incident stress counsellors Computer resources sentences: - How many ways are there to train SAR specialists and teams? - What types of support facilities are mentioned in the context? - What is the duration of a prolonged blast? - source_sentence: 'Sound funding decisions arise out of accurate assessments made of the SAR system. To measure the performance or effectiveness of a SAR system usually requires collecting information or statistics and establishing agreed-upon goals. All pertinent information should be collected, including where the system failed to perform as it should have; failures and successes provide valuable information in assessing effectiveness and determining means to improve. ' sentences: - What is required to measure the performance or effectiveness of a SAR system? - What is the purpose of having an SRR? - What is the effect of decreasing track spacing on the area that can be searched? 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: 0.7631578947368421 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9122807017543859 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9385964912280702 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9912280701754386 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7631578947368421 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.30409356725146197 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.18771929824561404 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09912280701754386 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7631578947368421 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.9122807017543859 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9385964912280702 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9912280701754386 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8800566604626379 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8442112225006964 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.8449422166527428 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.7456140350877193 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9210526315789473 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9385964912280702 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9912280701754386 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7456140350877193 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.30701754385964913 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.18771929824561404 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09912280701754386 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7456140350877193 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.9210526315789473 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9385964912280702 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9912280701754386 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8757357824813555 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8383040935672514 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.8389306599832915 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.7280701754385965 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8947368421052632 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9385964912280702 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.956140350877193 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7280701754385965 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2982456140350877 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.18771929824561406 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.0956140350877193 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7280701754385965 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8947368421052632 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9385964912280702 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.956140350877193 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8514949465138896 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8167397660818715 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.8197472848788638 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.6842105263157895 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8596491228070176 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8947368421052632 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9385964912280702 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6842105263157895 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.28654970760233917 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17894736842105263 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09385964912280703 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6842105263157895 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8596491228070176 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8947368421052632 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9385964912280702 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8139200097505314 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7736702868281816 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7777583689864392 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.6140350877192983 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7456140350877193 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8245614035087719 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8947368421052632 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6140350877192983 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.24853801169590642 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16491228070175437 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08947368421052632 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6140350877192983 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7456140350877193 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8245614035087719 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8947368421052632 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7479917679807845 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7017961570593151 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7073668567988093 name: Cosine Map@100 --- # BGE base Financial Matryoshka This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) on the json dataset. It maps sentences & paragraphs to a 1024-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-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 1024 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': 1024, '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("tessimago/bge-large-repmus-matryoshka") # Run inference sentences = [ 'Sound funding decisions arise out of accurate assessments made of the SAR system. To measure the performance or effectiveness of a SAR system usually requires collecting information or statistics and establishing agreed-upon goals. All pertinent information should be collected, including where the system failed to perform as it should have; failures and successes provide valuable information in assessing effectiveness and determining means to improve. ', 'What is required to measure the performance or effectiveness of a SAR system?', 'What is the effect of decreasing track spacing on the area that can be searched?', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # 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 | 0.7632 | | cosine_accuracy@3 | 0.9123 | | cosine_accuracy@5 | 0.9386 | | cosine_accuracy@10 | 0.9912 | | cosine_precision@1 | 0.7632 | | cosine_precision@3 | 0.3041 | | cosine_precision@5 | 0.1877 | | cosine_precision@10 | 0.0991 | | cosine_recall@1 | 0.7632 | | cosine_recall@3 | 0.9123 | | cosine_recall@5 | 0.9386 | | cosine_recall@10 | 0.9912 | | cosine_ndcg@10 | 0.8801 | | cosine_mrr@10 | 0.8442 | | **cosine_map@100** | **0.8449** | #### 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 | 0.7456 | | cosine_accuracy@3 | 0.9211 | | cosine_accuracy@5 | 0.9386 | | cosine_accuracy@10 | 0.9912 | | cosine_precision@1 | 0.7456 | | cosine_precision@3 | 0.307 | | cosine_precision@5 | 0.1877 | | cosine_precision@10 | 0.0991 | | cosine_recall@1 | 0.7456 | | cosine_recall@3 | 0.9211 | | cosine_recall@5 | 0.9386 | | cosine_recall@10 | 0.9912 | | cosine_ndcg@10 | 0.8757 | | cosine_mrr@10 | 0.8383 | | **cosine_map@100** | **0.8389** | #### 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 | 0.7281 | | cosine_accuracy@3 | 0.8947 | | cosine_accuracy@5 | 0.9386 | | cosine_accuracy@10 | 0.9561 | | cosine_precision@1 | 0.7281 | | cosine_precision@3 | 0.2982 | | cosine_precision@5 | 0.1877 | | cosine_precision@10 | 0.0956 | | cosine_recall@1 | 0.7281 | | cosine_recall@3 | 0.8947 | | cosine_recall@5 | 0.9386 | | cosine_recall@10 | 0.9561 | | cosine_ndcg@10 | 0.8515 | | cosine_mrr@10 | 0.8167 | | **cosine_map@100** | **0.8197** | #### 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 | 0.6842 | | cosine_accuracy@3 | 0.8596 | | cosine_accuracy@5 | 0.8947 | | cosine_accuracy@10 | 0.9386 | | cosine_precision@1 | 0.6842 | | cosine_precision@3 | 0.2865 | | cosine_precision@5 | 0.1789 | | cosine_precision@10 | 0.0939 | | cosine_recall@1 | 0.6842 | | cosine_recall@3 | 0.8596 | | cosine_recall@5 | 0.8947 | | cosine_recall@10 | 0.9386 | | cosine_ndcg@10 | 0.8139 | | cosine_mrr@10 | 0.7737 | | **cosine_map@100** | **0.7778** | #### 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 | 0.614 | | cosine_accuracy@3 | 0.7456 | | cosine_accuracy@5 | 0.8246 | | cosine_accuracy@10 | 0.8947 | | cosine_precision@1 | 0.614 | | cosine_precision@3 | 0.2485 | | cosine_precision@5 | 0.1649 | | cosine_precision@10 | 0.0895 | | cosine_recall@1 | 0.614 | | cosine_recall@3 | 0.7456 | | cosine_recall@5 | 0.8246 | | cosine_recall@10 | 0.8947 | | cosine_ndcg@10 | 0.748 | | cosine_mrr@10 | 0.7018 | | **cosine_map@100** | **0.7074** | ## Training Details ### Training Dataset #### json * Dataset: json * Size: 1,024 training samples * Columns: positive and anchor * Approximate statistics based on the first 1000 samples: | | positive | anchor | |:--------|:-------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | positive | anchor | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------| | The debriefing helps to ensure that all survivors are rescued, to attend to the physical welfare of each survivor, and to obtain information which may assist and improve SAR services. Proper debriefing techniques include:– due care to avoid worsening a survivor’s condition by excessive debriefing;– careful assessment of the survivor’s statements if the survivor is frightened or excited;– use of a calm voice in questioning;– avoidance of suggesting the answers when obtaining facts; and– explaining that the information requested is important for the success of the SAR operation, and possibly for future SAR operations. | What are some proper debriefing techniques used in SAR services? | | Communicating with passengers is more difficult in remote areas where phone service may be inadequate or lacking. If phones do exist, calling the airline or shipping company may be the best way to check in and find out information. In more populated areas, local agencies may have an emergency evacuation plan or other useful plan that can be implemented.IE961E.indb 21 6/28/2013 10:29:55 AM | What is a good way to check in and find out information in remote areas where phone service may be inadequate or lacking? | | Voice communication is the basis of telemedical advice. It allows free dialogue and contributes to the human relationship, which is crucial to any medical consultation. Text messages are a useful complement to the voice telemedical advice and add the reliability of writing. Facsimile allows the exchange of pictures or diagrams, which help to identify a symptom, describe a lesion or the method of treatment. Digital data transmissions (photographs or electrocardiogram) provide an objective and potentially crucial addition to descriptive and subjective clinical data. | What are the types of communication methods used in telemedical advice? | * Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 16 - `gradient_accumulation_steps`: 16 - `learning_rate`: 2e-05 - `num_train_epochs`: 4 - `lr_scheduler_type`: cosine - `warmup_ratio`: 0.1 - `bf16`: True - `tf32`: True - `load_best_model_at_end`: True - `optim`: adamw_torch_fused - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 16 - `eval_accumulation_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 4 - `max_steps`: -1 - `lr_scheduler_type`: cosine - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: True - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: True - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch_fused - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 | |:-------:|:-----:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| | 1.0 | 2 | 0.7826 | 0.8163 | 0.8230 | 0.6761 | 0.8359 | | 2.0 | 4 | 0.7739 | 0.8218 | 0.8282 | 0.6939 | 0.8459 | | 3.0 | 6 | 0.7740 | 0.8223 | 0.8409 | 0.7072 | 0.8457 | | **4.0** | **8** | **0.7778** | **0.8197** | **0.8389** | **0.7074** | **0.8449** | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.14 - Sentence Transformers: 3.1.0 - Transformers: 4.41.2 - PyTorch: 2.1.2+cu121 - Accelerate: 0.34.2 - 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} } ```