--- 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: We expect ME&T’s capital expenditures in 2024 to be around $2.0 billion to $2.5 billion. sentences: - What was the amount gained from the disposal of assets in 2022? - What is the expected capital expenditure for ME&T in 2024? - What is the expected total cost HP will incur from its Fiscal 2023 Plan, and how is it primarily divided? - source_sentence: Average invested capital is calculated as the sum of (i) the average of our total assets, (ii) the average LIFO reserve and (iii) the average accumulated depreciation and amortization; minus (i) the average taxes receivable, (ii) the average trade accounts payable, (iii) the average accrued salaries and wages and (iv) the average other current liabilities, excluding accrued income taxes. sentences: - What are the components and the effective tax rates for the year 2023 as reported in the financial statements? - How is average invested capital calculated for ROIC? - How did the interest income change in fiscal year 2023 compared to the previous year? - source_sentence: Return on Invested Capital ('ROIC') as of May 31, 2023 was 31.5% compared to 46.5% as of May 31, 2022. sentences: - How is NIKE's return on invested capital (ROIC) calculated, and what was its value as of May 31, 2023? - What role do medical directors play at outpatient dialysis centers, and what are their general qualifications? - What item number discusses legal proceedings in the report? - source_sentence: Net cash used in financing activities was $506.5 million in the year ended December 31, 2022, and increased to $656.5 million in the year ended December 31, 2023. sentences: - How has the change in foreign exchange rates affected cash and cash equivalents in 2023 and 2021? - What kind of financial documents are included in Part IV, Item 15(a)(1) of the Annual Report on Form 10-K? - How did the net cash used in financing activities in 2023 compare to 2022? - source_sentence: 'Alternative Payments Providers: These providers, such as closed commerce ecosystems, BNPL solutions and cryptocurrency platforms, often have a primary focus of enabling payments through ecommerce and mobile channels; however, they are expanding or may expand their offerings to the physical point of sale. These companies may process payments using in-house account transfers between parties, electronic funds transfer networks like the ACH, global or local networks like Visa, or some combination of the foregoing.' sentences: - What are some examples of alternative payments providers and how do they compete with Visa? - How much did the company's currently payable U.S. taxes amount to in 2023? - What considerations are involved in recording an uncertain tax position? 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: 0.6885714285714286 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8328571428571429 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8742857142857143 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9142857142857143 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6885714285714286 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2776190476190476 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17485714285714282 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09142857142857141 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6885714285714286 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8328571428571429 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8742857142857143 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9142857142857143 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8044897381040067 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7690017006802718 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.772240177124622 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.6971428571428572 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8342857142857143 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8742857142857143 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9071428571428571 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6971428571428572 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27809523809523806 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17485714285714282 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09071428571428569 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6971428571428572 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8342857142857143 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8742857142857143 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9071428571428571 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8044496489287004 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7712602040816322 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7750129601859859 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: 0.8257142857142857 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8714285714285714 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: 0.2752380952380953 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17428571428571427 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: 0.8257142857142857 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8714285714285714 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.91 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8034440275222344 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7690856009070293 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7724648546606009 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.6742857142857143 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.81 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8542857142857143 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6742857142857143 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17085714285714282 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6742857142857143 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.81 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8542857142857143 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7881399973034273 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7522210884353742 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7560032496112399 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.6385714285714286 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7671428571428571 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8242857142857143 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.87 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6385714285714286 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2557142857142857 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16485714285714284 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.087 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6385714285714286 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7671428571428571 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8242857142857143 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.87 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7528845651704559 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7154948979591831 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7205565552029373 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("kperkins411/bge-base-financial-matryoshka") # Run inference sentences = [ 'Alternative Payments Providers: These providers, such as closed commerce ecosystems, BNPL solutions and cryptocurrency platforms, often have a primary focus of enabling payments through ecommerce and mobile channels; however, they are expanding or may expand their offerings to the physical point of sale. These companies may process payments using in-house account transfers between parties, electronic funds transfer networks like the ACH, global or local networks like Visa, or some combination of the foregoing.', 'What are some examples of alternative payments providers and how do they compete with Visa?', "How much did the company's currently payable U.S. taxes amount to in 2023?", ] 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 | 0.6886 | | cosine_accuracy@3 | 0.8329 | | cosine_accuracy@5 | 0.8743 | | cosine_accuracy@10 | 0.9143 | | cosine_precision@1 | 0.6886 | | cosine_precision@3 | 0.2776 | | cosine_precision@5 | 0.1749 | | cosine_precision@10 | 0.0914 | | cosine_recall@1 | 0.6886 | | cosine_recall@3 | 0.8329 | | cosine_recall@5 | 0.8743 | | cosine_recall@10 | 0.9143 | | cosine_ndcg@10 | 0.8045 | | cosine_mrr@10 | 0.769 | | **cosine_map@100** | **0.7722** | #### 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 | 0.6971 | | cosine_accuracy@3 | 0.8343 | | cosine_accuracy@5 | 0.8743 | | cosine_accuracy@10 | 0.9071 | | cosine_precision@1 | 0.6971 | | cosine_precision@3 | 0.2781 | | cosine_precision@5 | 0.1749 | | cosine_precision@10 | 0.0907 | | cosine_recall@1 | 0.6971 | | cosine_recall@3 | 0.8343 | | cosine_recall@5 | 0.8743 | | cosine_recall@10 | 0.9071 | | cosine_ndcg@10 | 0.8044 | | cosine_mrr@10 | 0.7713 | | **cosine_map@100** | **0.775** | #### 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 | 0.8257 | | cosine_accuracy@5 | 0.8714 | | cosine_accuracy@10 | 0.91 | | cosine_precision@1 | 0.6914 | | cosine_precision@3 | 0.2752 | | cosine_precision@5 | 0.1743 | | cosine_precision@10 | 0.091 | | cosine_recall@1 | 0.6914 | | cosine_recall@3 | 0.8257 | | cosine_recall@5 | 0.8714 | | cosine_recall@10 | 0.91 | | cosine_ndcg@10 | 0.8034 | | cosine_mrr@10 | 0.7691 | | **cosine_map@100** | **0.7725** | #### 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 | 0.6743 | | cosine_accuracy@3 | 0.81 | | cosine_accuracy@5 | 0.8543 | | cosine_accuracy@10 | 0.9 | | cosine_precision@1 | 0.6743 | | cosine_precision@3 | 0.27 | | cosine_precision@5 | 0.1709 | | cosine_precision@10 | 0.09 | | cosine_recall@1 | 0.6743 | | cosine_recall@3 | 0.81 | | cosine_recall@5 | 0.8543 | | cosine_recall@10 | 0.9 | | cosine_ndcg@10 | 0.7881 | | cosine_mrr@10 | 0.7522 | | **cosine_map@100** | **0.756** | #### 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 | 0.6386 | | cosine_accuracy@3 | 0.7671 | | cosine_accuracy@5 | 0.8243 | | cosine_accuracy@10 | 0.87 | | cosine_precision@1 | 0.6386 | | cosine_precision@3 | 0.2557 | | cosine_precision@5 | 0.1649 | | cosine_precision@10 | 0.087 | | cosine_recall@1 | 0.6386 | | cosine_recall@3 | 0.7671 | | cosine_recall@5 | 0.8243 | | cosine_recall@10 | 0.87 | | cosine_ndcg@10 | 0.7529 | | cosine_mrr@10 | 0.7155 | | **cosine_map@100** | **0.7206** | ## 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 | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Activities related to sales before 2023 experienced adjustments due to changes in estimates, impacting the rebates and chargebacks accounts, and led to an ending balance of $4,493 million for the year 2023. | What adjustments were made to the rebates and chargebacks balances for previous years' sales and how did they affect the end of year balance in 2023? | | We’re focused on making hosting just as popular as traveling on Airbnb. We will continue to invest in growing the size and quality of our Host community. We plan to attract more Hosts globally by expanding use cases and supporting all different types of Hosts, including those who host occasionally. | What is Airbnb's long-term corporate strategy regarding hosting? | | Due to protectionist measures in various regions, Nike has experienced increased product costs. The company responds by monitoring trends, engaging in processes to mitigate restrictions, and advocating for trade liberalization in trade agreements. | What challenges related to trade protectionism has Nike faced, and what measures has the company taken in response? | * 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 | |:--------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| | 0.96 | 3 | - | 0.7116 | 0.7341 | 0.7448 | 0.6550 | 0.7455 | | 1.92 | 6 | - | 0.7317 | 0.7520 | 0.7586 | 0.6975 | 0.7591 | | 2.88 | 9 | - | 0.7334 | 0.7553 | 0.7631 | 0.7039 | 0.7630 | | 3.2 | 10 | 3.3636 | - | - | - | - | - | | **3.84** | **12** | **-** | **0.7368** | **0.759** | **0.7634** | **0.7054** | **0.7638** | | 0.96 | 3 | - | 0.7415 | 0.7601 | 0.7672 | 0.7102 | 0.7661 | | 1.92 | 6 | - | 0.7486 | 0.7683 | 0.7720 | 0.7205 | 0.7718 | | 2.88 | 9 | - | 0.7556 | 0.7718 | 0.7750 | 0.7215 | 0.7717 | | 3.2 | 10 | 1.66 | - | - | - | - | - | | **3.84** | **12** | **-** | **0.756** | **0.7725** | **0.775** | **0.7206** | **0.7722** | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.11.9 - 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} } ```