--- base_model: BAAI/bge-small-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: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: BAAI BGE small en v1.5 ESG results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 384 type: dim_384 metrics: - type: cosine_accuracy@1 value: 0.786984742476608 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9269156199949422 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.944617718958105 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9597066509314676 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.786984742476608 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3089718733316474 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.18892354379162102 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09597066509314678 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.021860687291016895 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.025747656110970626 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.026239381082169593 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.026658518081429664 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.19459455903970813 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8588156921146056 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.023886995279989515 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.7815055213689623 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9236280873303548 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9421731433870016 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9596223552221191 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7815055213689623 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.30787602911011824 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.18843462867740032 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09596223552221193 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.021708486704693403 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.025656335759176533 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.026171476205194496 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.02665617653394776 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.19396598426779785 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8550811914864019 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.023784308256522512 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.7713057405378067 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9141869678833348 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9346708252549946 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9532158813116413 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7713057405378067 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3047289892944449 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.18693416505099894 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09532158813116413 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.021425159459383523 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.025394082441203752 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.025963078479305412 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.026478218925323375 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.192049680708846 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8456702445512195 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.023531692780408037 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.7428137907780494 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.892438674871449 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9184860490601029 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9411615948748209 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7428137907780494 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.297479558290483 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1836972098120206 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09411615948748209 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.02063371641050138 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.024789963190873596 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.02551350136278064 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.026143377635411698 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.18745029665008597 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8220114494981732 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.022884160441989647 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 32 type: dim_32 metrics: - type: cosine_accuracy@1 value: 0.6668633566551463 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8242434460085981 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8640310208210402 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8987608530725786 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6668633566551463 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27474781533619935 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17280620416420805 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08987608530725787 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.018523982129309623 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.022895651278016623 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.024000861689473345 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.02496557925201608 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.17367624271978654 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7532998425142056 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.02100792923667254 name: Cosine Map@100 --- # BAAI BGE small en v1.5 ESG This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5). 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:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) - **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': True}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, '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("elsayovita/bge-small-en-v1.5-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 | 0.787 | | cosine_accuracy@3 | 0.9269 | | cosine_accuracy@5 | 0.9446 | | cosine_accuracy@10 | 0.9597 | | cosine_precision@1 | 0.787 | | cosine_precision@3 | 0.309 | | cosine_precision@5 | 0.1889 | | cosine_precision@10 | 0.096 | | cosine_recall@1 | 0.0219 | | cosine_recall@3 | 0.0257 | | cosine_recall@5 | 0.0262 | | cosine_recall@10 | 0.0267 | | cosine_ndcg@10 | 0.1946 | | cosine_mrr@10 | 0.8588 | | **cosine_map@100** | **0.0239** | #### 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 | 0.7815 | | cosine_accuracy@3 | 0.9236 | | cosine_accuracy@5 | 0.9422 | | cosine_accuracy@10 | 0.9596 | | cosine_precision@1 | 0.7815 | | cosine_precision@3 | 0.3079 | | cosine_precision@5 | 0.1884 | | cosine_precision@10 | 0.096 | | cosine_recall@1 | 0.0217 | | cosine_recall@3 | 0.0257 | | cosine_recall@5 | 0.0262 | | cosine_recall@10 | 0.0267 | | cosine_ndcg@10 | 0.194 | | cosine_mrr@10 | 0.8551 | | **cosine_map@100** | **0.0238** | #### 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 | 0.7713 | | cosine_accuracy@3 | 0.9142 | | cosine_accuracy@5 | 0.9347 | | cosine_accuracy@10 | 0.9532 | | cosine_precision@1 | 0.7713 | | cosine_precision@3 | 0.3047 | | cosine_precision@5 | 0.1869 | | cosine_precision@10 | 0.0953 | | cosine_recall@1 | 0.0214 | | cosine_recall@3 | 0.0254 | | cosine_recall@5 | 0.026 | | cosine_recall@10 | 0.0265 | | cosine_ndcg@10 | 0.192 | | cosine_mrr@10 | 0.8457 | | **cosine_map@100** | **0.0235** | #### 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 | 0.7428 | | cosine_accuracy@3 | 0.8924 | | cosine_accuracy@5 | 0.9185 | | cosine_accuracy@10 | 0.9412 | | cosine_precision@1 | 0.7428 | | cosine_precision@3 | 0.2975 | | cosine_precision@5 | 0.1837 | | cosine_precision@10 | 0.0941 | | cosine_recall@1 | 0.0206 | | cosine_recall@3 | 0.0248 | | cosine_recall@5 | 0.0255 | | cosine_recall@10 | 0.0261 | | cosine_ndcg@10 | 0.1875 | | cosine_mrr@10 | 0.822 | | **cosine_map@100** | **0.0229** | #### 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 | 0.6669 | | cosine_accuracy@3 | 0.8242 | | cosine_accuracy@5 | 0.864 | | cosine_accuracy@10 | 0.8988 | | cosine_precision@1 | 0.6669 | | cosine_precision@3 | 0.2747 | | cosine_precision@5 | 0.1728 | | cosine_precision@10 | 0.0899 | | cosine_recall@1 | 0.0185 | | cosine_recall@3 | 0.0229 | | cosine_recall@5 | 0.024 | | cosine_recall@10 | 0.025 | | cosine_ndcg@10 | 0.1737 | | cosine_mrr@10 | 0.7533 | | **cosine_map@100** | **0.021** | ## 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`: 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 - `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 | 4.3426 | - | - | - | - | - | | 0.8625 | 20 | 2.7083 | - | - | - | - | - | | 1.0350 | 24 | - | 0.0229 | 0.0233 | 0.0195 | 0.0234 | 0.0220 | | 1.2264 | 30 | 2.6835 | - | - | - | - | - | | 1.6577 | 40 | 2.1702 | - | - | - | - | - | | 1.9164 | 46 | - | 0.0230 | 0.0234 | 0.0197 | 0.0235 | 0.0221 | | 0.4313 | 10 | 2.2406 | - | - | - | - | - | | 0.8625 | 20 | 1.8606 | - | - | - | - | - | | 1.0350 | 24 | - | 0.0233 | 0.0236 | 0.0204 | 0.0237 | 0.0225 | | 1.2264 | 30 | 2.0645 | - | - | - | - | - | | 1.6577 | 40 | 1.6752 | - | - | - | - | - | | 2.0458 | 49 | - | 0.0235 | 0.0237 | 0.0208 | 0.0238 | 0.0228 | | 2.0216 | 50 | 1.7855 | - | - | - | - | - | | 2.4528 | 60 | 1.7333 | - | - | - | - | - | | 2.8841 | 70 | 1.5116 | - | - | - | - | - | | 3.0566 | 74 | - | 0.0235 | 0.0238 | 0.0210 | 0.0239 | 0.0229 | | 3.2480 | 80 | 1.7812 | - | - | - | - | - | | 3.6792 | 90 | 1.4886 | - | - | - | - | - | | **3.7655** | **92** | **-** | **0.0235** | **0.0238** | **0.021** | **0.0239** | **0.0229** | * 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} } ```