--- base_model: TaylorAI/bge-micro-v2 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:11863 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: In the fiscal year 2022, the emissions were categorized into different scopes, with each scope representing a specific source of emissions sentences: - 'Question: What is NetLink proactive in identifying to be more efficient in? ' - What standard is the Environment, Health, and Safety Management System (EHSMS) audited to by a third-party accredited certification body at the operational assets level of CLI? - What do the different scopes represent in terms of emissions in the fiscal year 2022? - source_sentence: NetLink is committed to protecting the security of all information and information systems, including both end-user data and corporate data. To this end, management ensures that the appropriate IT policies, personal data protection policy, risk mitigation strategies, cyber security programmes, systems, processes, and controls are in place to protect our IT systems and confidential data sentences: - '"What recognition did NetLink receive in FY22?"' - What measures does NetLink have in place to protect the security of all information and information systems, including end-user data and corporate data? - 'Question: What does Disclosure 102-10 discuss regarding the organization and its supply chain?' - source_sentence: In the domain of economic performance, the focus is on the financial health and growth of the organization, ensuring sustainable profitability and value creation for stakeholders sentences: - What does NetLink prioritize by investing in its network to ensure reliability and quality of infrastructure? - What percentage of the total energy was accounted for by heat, steam, and chilled water in 2021 according to the given information? - What is the focus in the domain of economic performance, ensuring sustainable profitability and value creation for stakeholders? - source_sentence: Disclosure 102-41 discusses collective bargaining agreements and is found on page 98 sentences: - What topic is discussed in Disclosure 102-41 on page 98 of the document? - What was the number of cases in 2021, following a decrease from 42 cases in 2020? - What type of data does GRI 101 provide in relation to connecting the nation? - source_sentence: Employee health and well-being has never been more topical than it was in the past year. We understand that people around the world, including our employees, have been increasingly exposed to factors affecting their physical and mental wellbeing. We are committed to creating an environment that supports our employees and ensures they feel valued and have a sense of belonging. We utilised sentences: - What aspect of the standard covers the evaluation of the management approach? - 'Question: What is the company''s commitment towards its employees'' health and well-being based on the provided context information?' - What types of skills does NetLink focus on developing through their training and development opportunities for employees? model-index: - name: BGE micro v2 ESG results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 384 type: dim_384 metrics: - type: cosine_accuracy@1 value: 0.7549523729242181 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8991823316193206 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9237123830397033 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9447020146674534 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7549523729242181 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2997274438731068 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1847424766079407 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09447020146674537 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.020970899247894956 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.02497728698942558 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.025658677306658433 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.026241722629651493 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.18912117167223944 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8309359566693303 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.023120117824201005 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.7496417432352693 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8958105032453848 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9187389361881481 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9417516648402596 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7496417432352693 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2986035010817949 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1837477872376296 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09417516648402599 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.020823381756535267 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.02488362509014959 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.025520526005226342 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.026159768467784998 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.188171652806899 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8261983036492017 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.022991454812532088 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.7355643597740875 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8874652280198938 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9105622523813538 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9341650509989041 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7355643597740875 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2958217426732979 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1821124504762708 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09341650509989044 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.02043234332705799 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.0246518118894415 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.025293395899482058 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.02594902919441401 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.18580500893220617 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8144083444724101 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.022667974495178208 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.6972098120205682 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8493635673944196 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8830818511337772 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.913175419371154 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6972098120205682 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.28312118913147316 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17661637022675547 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09131754193711542 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.019366939222793565 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.023593432427622775 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.024530051420382712 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.02536598387142095 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.1787893349481174 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7792686076088251 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.021712360244980362 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 32 type: dim_32 metrics: - type: cosine_accuracy@1 value: 0.5974036921520695 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7523392059344179 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.7970159318890668 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8448115990896063 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.5974036921520695 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.25077973531147263 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.15940318637781337 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08448115990896064 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.016594547004224157 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.02089831127595606 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.022139331441362976 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.023466988863600182 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.15933281345013575 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.6849689711507925 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.019142044257794796 name: Cosine Map@100 --- # BGE micro v2 ESG This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [TaylorAI/bge-micro-v2](https://huggingface.co/TaylorAI/bge-micro-v2). It maps sentences & paragraphs to a 384-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:** [TaylorAI/bge-micro-v2](https://huggingface.co/TaylorAI/bge-micro-v2) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 384 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': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## 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("elsayovita/bge-micro-v2-esg-v2") # Run inference sentences = [ 'Employee health and well-being has never been more topical than it was in the past year. We understand that people around the world, including our employees, have been increasingly exposed to factors affecting their physical and mental wellbeing. We are committed to creating an environment that supports our employees and ensures they feel valued and have a sense of belonging. We utilised', "Question: What is the company's commitment towards its employees' health and well-being based on the provided context information?", 'What types of skills does NetLink focus on developing through their training and development opportunities for employees?', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 384] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Information Retrieval * Dataset: `dim_384` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.755 | | cosine_accuracy@3 | 0.8992 | | cosine_accuracy@5 | 0.9237 | | cosine_accuracy@10 | 0.9447 | | cosine_precision@1 | 0.755 | | cosine_precision@3 | 0.2997 | | cosine_precision@5 | 0.1847 | | cosine_precision@10 | 0.0945 | | cosine_recall@1 | 0.021 | | cosine_recall@3 | 0.025 | | cosine_recall@5 | 0.0257 | | cosine_recall@10 | 0.0262 | | cosine_ndcg@10 | 0.1891 | | cosine_mrr@10 | 0.8309 | | **cosine_map@100** | **0.0231** | #### 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 | 0.7496 | | cosine_accuracy@3 | 0.8958 | | cosine_accuracy@5 | 0.9187 | | cosine_accuracy@10 | 0.9418 | | cosine_precision@1 | 0.7496 | | cosine_precision@3 | 0.2986 | | cosine_precision@5 | 0.1837 | | cosine_precision@10 | 0.0942 | | cosine_recall@1 | 0.0208 | | cosine_recall@3 | 0.0249 | | cosine_recall@5 | 0.0255 | | cosine_recall@10 | 0.0262 | | cosine_ndcg@10 | 0.1882 | | cosine_mrr@10 | 0.8262 | | **cosine_map@100** | **0.023** | #### 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 | 0.7356 | | cosine_accuracy@3 | 0.8875 | | cosine_accuracy@5 | 0.9106 | | cosine_accuracy@10 | 0.9342 | | cosine_precision@1 | 0.7356 | | cosine_precision@3 | 0.2958 | | cosine_precision@5 | 0.1821 | | cosine_precision@10 | 0.0934 | | cosine_recall@1 | 0.0204 | | cosine_recall@3 | 0.0247 | | cosine_recall@5 | 0.0253 | | cosine_recall@10 | 0.0259 | | cosine_ndcg@10 | 0.1858 | | cosine_mrr@10 | 0.8144 | | **cosine_map@100** | **0.0227** | #### 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 | 0.6972 | | cosine_accuracy@3 | 0.8494 | | cosine_accuracy@5 | 0.8831 | | cosine_accuracy@10 | 0.9132 | | cosine_precision@1 | 0.6972 | | cosine_precision@3 | 0.2831 | | cosine_precision@5 | 0.1766 | | cosine_precision@10 | 0.0913 | | cosine_recall@1 | 0.0194 | | cosine_recall@3 | 0.0236 | | cosine_recall@5 | 0.0245 | | cosine_recall@10 | 0.0254 | | cosine_ndcg@10 | 0.1788 | | cosine_mrr@10 | 0.7793 | | **cosine_map@100** | **0.0217** | #### Information Retrieval * Dataset: `dim_32` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.5974 | | cosine_accuracy@3 | 0.7523 | | cosine_accuracy@5 | 0.797 | | cosine_accuracy@10 | 0.8448 | | cosine_precision@1 | 0.5974 | | cosine_precision@3 | 0.2508 | | cosine_precision@5 | 0.1594 | | cosine_precision@10 | 0.0845 | | cosine_recall@1 | 0.0166 | | cosine_recall@3 | 0.0209 | | cosine_recall@5 | 0.0221 | | cosine_recall@10 | 0.0235 | | cosine_ndcg@10 | 0.1593 | | cosine_mrr@10 | 0.685 | | **cosine_map@100** | **0.0191** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 11,863 training samples * Columns: context and question * Approximate statistics based on the first 1000 samples: | | context | question | |:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | context | question | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------| | The engagement with key stakeholders involves various topics and methods throughout the year | Question: What does the engagement with key stakeholders involve throughout the year? | | For unitholders and analysts, the focus is on business and operations, the release of financial results, and the overall performance and announcements | Question: What is the focus for unitholders and analysts in terms of business and operations, financial results, performance, and announcements? | | These are communicated through press releases and other required disclosures via SGXNet and NetLink's website | What platform is used to communicate press releases and required disclosures for NetLink? | * Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 384, 256, 128, 64, 32 ], "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`: False - `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`: False - `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 - `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_32_cosine_map@100 | dim_384_cosine_map@100 | dim_64_cosine_map@100 | |:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|:---------------------:| | 0.4313 | 10 | 5.2501 | - | - | - | - | - | | 0.8625 | 20 | 3.4967 | - | - | - | - | - | | 1.0350 | 24 | - | 0.0221 | 0.0224 | 0.0185 | 0.0226 | 0.0210 | | 1.2264 | 30 | 3.1196 | - | - | - | - | - | | 1.6577 | 40 | 2.4428 | - | - | - | - | - | | 2.0458 | 49 | - | 0.0226 | 0.0229 | 0.0189 | 0.0230 | 0.0215 | | 2.0216 | 50 | 2.2222 | - | - | - | - | - | | 2.4528 | 60 | 2.3441 | - | - | - | - | - | | 2.8841 | 70 | 2.0096 | - | - | - | - | - | | 3.0566 | 74 | - | 0.0227 | 0.0230 | 0.0191 | 0.0231 | 0.0217 | | 3.2480 | 80 | 2.3019 | - | - | - | - | - | | 3.6792 | 90 | 1.9538 | - | - | - | - | - | | **3.7655** | **92** | **-** | **0.0227** | **0.023** | **0.0191** | **0.0231** | **0.0217** | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.1 - Transformers: 4.42.4 - PyTorch: 2.4.0+cu121 - Accelerate: 0.32.1 - 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} } ```