--- 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: Total net additions to property and equipment for AWS in 2023 amounted to $24,843 million. sentences: - What technological feature helps protect digital transactions in the Visa Token Service? - What was the total net addition to property and equipment for AWS in the year 2023? - By what proportion did net cash used in financing activities increase from 2022 to 2023? - source_sentence: 'Leases generally contain one or more of the following options, which the Company can exercise at the end of the initial term: (a) renew the lease for a defined number of years at the then-fair market rental rate or rate stipulated in the lease agreement; (b) purchase the property at the then-fair market value or purchase price stated in the agreement; or (c) a right of first refusal in the event of a third-party offer.' sentences: - What are the requirements for health insurers and group health plans in providing cost estimates to consumers? - What options does the company have at the end of the lease term for their leased properties? - How much did the company incur in intangible amortization costs related to the eOne acquisition in 2022? - source_sentence: We recorded an acquisition termination cost of $1.35 billion in fiscal year 2023 reflecting the write-off of the prepayment provided at signing. sentences: - How much did NVIDIA record as an acquisition termination cost in fiscal year 2023 related to the Arm Share Purchase Agreement? - What is included in the consolidated financial statements and accompanying notes mentioned in Part IV, Item 15(a)(1) of the Annual Report on Form 10-K? - What risks are associated with projecting the effectiveness of internal controls into future periods as mentioned? - source_sentence: Item 8 is labeled as Financial Statements and Supplementary Data. sentences: - What was the percentage of trading days in 2023 where trading-related revenue was recorded as positive? - How is the discount rate for the Family Dollar goodwill impairment evaluation determined? - What is the title of Item 8 in the financial document? - source_sentence: Details about legal proceedings are included in Part II, Item 8, "Financial Statements and Supplementary Data" of the Annual Report on Form 10-K, under the caption "Legal Proceedings". sentences: - Where can details about legal proceedings be located in an Annual Report on Form 10-K? - How many stores did AutoZone operate in the United States as of August 26, 2023? - 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? 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: 0.7071428571428572 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8414285714285714 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.88 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9314285714285714 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7071428571428572 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.28047619047619043 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.176 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09314285714285712 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7071428571428572 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8414285714285714 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.88 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9314285714285714 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8207437059171859 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7853486394557823 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7881907906804949 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.6957142857142857 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8385714285714285 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8757142857142857 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.93 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6957142857142857 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2795238095238095 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17514285714285713 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09299999999999999 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6957142857142857 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8385714285714285 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8757142857142857 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.93 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8149439460863356 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7780714285714285 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.781021025356189 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.6885714285714286 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.83 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: 0.27666666666666667 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: 0.83 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: 0.8060991379418679 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7710873015873015 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7751792513774886 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.6771428571428572 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8214285714285714 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8614285714285714 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9142857142857143 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6771428571428572 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2738095238095238 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17228571428571426 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09142857142857141 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6771428571428572 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8214285714285714 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8614285714285714 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9142857142857143 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7979494993398927 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7605890022675734 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7639633810343436 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.6557142857142857 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7871428571428571 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8271428571428572 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8714285714285714 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6557142857142857 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2623809523809524 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1654285714285714 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08714285714285713 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6557142857142857 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7871428571428571 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8271428571428572 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8714285714285714 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7664083634078753 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7326604308390022 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7375736792740525 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("dustyatx/bge-base-financial-matryoshka") # Run inference sentences = [ 'Details about legal proceedings are included in Part II, Item 8, "Financial Statements and Supplementary Data" of the Annual Report on Form 10-K, under the caption "Legal Proceedings".', 'Where can details about legal proceedings be located in an Annual Report on Form 10-K?', 'How many stores did AutoZone operate in the United States as of August 26, 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 | 0.7071 | | cosine_accuracy@3 | 0.8414 | | cosine_accuracy@5 | 0.88 | | cosine_accuracy@10 | 0.9314 | | cosine_precision@1 | 0.7071 | | cosine_precision@3 | 0.2805 | | cosine_precision@5 | 0.176 | | cosine_precision@10 | 0.0931 | | cosine_recall@1 | 0.7071 | | cosine_recall@3 | 0.8414 | | cosine_recall@5 | 0.88 | | cosine_recall@10 | 0.9314 | | cosine_ndcg@10 | 0.8207 | | cosine_mrr@10 | 0.7853 | | **cosine_map@100** | **0.7882** | #### 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 | 0.6957 | | cosine_accuracy@3 | 0.8386 | | cosine_accuracy@5 | 0.8757 | | cosine_accuracy@10 | 0.93 | | cosine_precision@1 | 0.6957 | | cosine_precision@3 | 0.2795 | | cosine_precision@5 | 0.1751 | | cosine_precision@10 | 0.093 | | cosine_recall@1 | 0.6957 | | cosine_recall@3 | 0.8386 | | cosine_recall@5 | 0.8757 | | cosine_recall@10 | 0.93 | | cosine_ndcg@10 | 0.8149 | | cosine_mrr@10 | 0.7781 | | **cosine_map@100** | **0.781** | #### 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 | 0.6886 | | cosine_accuracy@3 | 0.83 | | cosine_accuracy@5 | 0.8743 | | cosine_accuracy@10 | 0.9143 | | cosine_precision@1 | 0.6886 | | cosine_precision@3 | 0.2767 | | cosine_precision@5 | 0.1749 | | cosine_precision@10 | 0.0914 | | cosine_recall@1 | 0.6886 | | cosine_recall@3 | 0.83 | | cosine_recall@5 | 0.8743 | | cosine_recall@10 | 0.9143 | | cosine_ndcg@10 | 0.8061 | | cosine_mrr@10 | 0.7711 | | **cosine_map@100** | **0.7752** | #### 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 | 0.6771 | | cosine_accuracy@3 | 0.8214 | | cosine_accuracy@5 | 0.8614 | | cosine_accuracy@10 | 0.9143 | | cosine_precision@1 | 0.6771 | | cosine_precision@3 | 0.2738 | | cosine_precision@5 | 0.1723 | | cosine_precision@10 | 0.0914 | | cosine_recall@1 | 0.6771 | | cosine_recall@3 | 0.8214 | | cosine_recall@5 | 0.8614 | | cosine_recall@10 | 0.9143 | | cosine_ndcg@10 | 0.7979 | | cosine_mrr@10 | 0.7606 | | **cosine_map@100** | **0.764** | #### 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 | 0.6557 | | cosine_accuracy@3 | 0.7871 | | cosine_accuracy@5 | 0.8271 | | cosine_accuracy@10 | 0.8714 | | cosine_precision@1 | 0.6557 | | cosine_precision@3 | 0.2624 | | cosine_precision@5 | 0.1654 | | cosine_precision@10 | 0.0871 | | cosine_recall@1 | 0.6557 | | cosine_recall@3 | 0.7871 | | cosine_recall@5 | 0.8271 | | cosine_recall@10 | 0.8714 | | cosine_ndcg@10 | 0.7664 | | cosine_mrr@10 | 0.7327 | | **cosine_map@100** | **0.7376** | ## 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 | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------| | The company must continuously strengthen its capabilities in marketing and innovation to compete in a digital environment and maintain brand loyalty and marketallability. In addition, it is increasing its investments in e-commerce to support retail and meal delivery services, offering more package sizes that are fit-for-purpose for online sales and shifting more consumer and trade promotions to digital. | What strategies is the company employing to enhance its competitiveness in a digital environment? | | Fedflowing expanded or relocated its hub and linehaul network, FedEx Ground also introduced new safety technologies, set new driver standards, and made operational enhancements for safer handling of heavy items. | What specific changes has FedEx Ground made for vehicle and driver safety? | | The debt financing, which is being provided by a syndicate of Chinese financial institutions, contains certain covenants and a maximum borrowing limit of ¥29.7 billion RMB (approximately $4.2 billion). | What is the maximum borrowing limit of the debt financing provided by the syndicate of Chinese financial institutions for Universal Beijing Resort? | * 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 - `torch_empty_cache_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 - `eval_on_start`: False - `eval_use_gather_object`: 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 | |:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| | 0.8122 | 10 | 1.5212 | - | - | - | - | - | | 0.9746 | 12 | - | 0.7439 | 0.7556 | 0.7670 | 0.7142 | 0.7717 | | 1.6244 | 20 | 0.6418 | - | - | - | - | - | | 1.9492 | 24 | - | 0.7592 | 0.7743 | 0.7787 | 0.7331 | 0.7839 | | 2.4365 | 30 | 0.4411 | - | - | - | - | - | | 2.9239 | 36 | - | 0.7623 | 0.7757 | 0.7816 | 0.7365 | 0.7902 | | 3.2487 | 40 | 0.3917 | - | - | - | - | - | | **3.8985** | **48** | **-** | **0.764** | **0.7752** | **0.781** | **0.7376** | **0.7882** | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.11.9 - Sentence Transformers: 3.0.1 - Transformers: 4.44.2 - PyTorch: 2.4.0+cu121 - Accelerate: 0.33.0 - Datasets: 2.21.0 - 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} } ```