--- language: - en license: apache-2.0 tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:3692 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss base_model: nomic-ai/modernbert-embed-base widget: - source_sentence: '| :-- | :--: | :--: || After the coming into force date | 1.00 | 1.00 || 6 April 2014 - coming into force date | 1.30 | 1.30 || 1 Oct 2010 - 5 April 2014 | 1.40 | 1.40 || 6 April 2006 - 30 Sept 2010 | 1.67 | 1.67 || Pre 6 April 2006 | 1.67 | 1.67 |' sentences: - What are the applicable building regulation change factors for different time periods starting from pre-6 April 2006 to the current date? - When are protected lobbies or corridors required for staircases in buildings with multiple storeys above ground level? - What are the standards for repairing, reconstructing, and altering existing drains and sewers? - source_sentence: The door has a minimum clear opening width of 850 mm , when measured in accordance with Diagram 3.2.g.Where there are double doors, the main (or leading) leaf provides the required minimum clear opening width.A minimum 200 mm nib is provided to the following edge of the door and the extra width created by the nib is maintained for a minimum of 1500 mm beyond it.[IMAGE]h.The door is located reasonably centrally within the thickness of the wall while ensuring that the depth of the reveal on the leading face of the door (usually the inside) is a maximum of 200 mm .i.The threshold is an accessible threshold.j.Where there is a lobby or porch, the doors are a minimum of 1500 mm apart and there is a minimum of 1500 mm between door swings.k.Door entry controls, where provided, are mounted 900-1000mm above finished ground level a minimum of 300 mm away from any external return corner.l.A fused spur, suitable for the fitting of a powered door opener, is provided on the hinge side of the door.# Other external doors3.23 All other external doors - including doors to and from a private garden, balcony, terrace, garage, carport, conservatory or storage area that is integral with, or connected, the dwelling comply with provisions $f$. to $k$. of paragraph 3.22 and should have a minimum 300 m nib to the leading edge of the door with the extra width created by this nib extending for a minimum 1800 mm beyond it.## Circulation areas, internal doorways and storage## Hall and door widths3.24 To facilitate wheelchair movement into and between rooms, internal halls and doors should comply with all of the following (see Diagram 3.4). sentences: - What are the minimum clear opening width and placement requirements for doors according to the UK building regulations? - What are the material class and limits for rooms and circulation spaces in buildings, excluding protected stairways? - What is the purpose of the Building and Engineering Services Association's DW/144 Specification for Sheet Metal Ductwork as referenced in the UK building regulations? - source_sentence: a.Every individual dwelling complies with all of the following conditions.i.The dwelling primary energy rate must not exceed the individual dwelling's target primary energy rate.ii.The dwelling emission rate must not exceed the individual dwelling's target emission rate.iii.The dwelling fabric energy efficiency rate must not exceed the individual dwelling's target fabric energy efficiency rate.ORb.All of the following are met.i. sentences: - What are the requirements for a parking space in a dwelling to accommodate a wheelchair user? - What are the fire separation requirements for a garage attached to a dwellinghouse? - What criteria must a dwelling meet to comply with target energy and emission rates in UK building regulations? - source_sentence: ISBN 071760 4136.The Workplace (Health, Safety and Welfare) Regulations 1992 apply to the common parts of flats and similar buildings if people such as cleaners and caretakers are employed to work in these common parts.Where the requirements of the Building Regulations that are covered by this Part do not apply to dwellings, the provisions may still be required in the situations described above in order to satisfy the Workplace Regulations.# USE OF GUIDANCE# The RequirementsThis Approved Document, which took effect on 1 July 2003, deals with the Requirements of Part E of Schedule 1 to the Building Regulations 2010.## Requirement## Protection against sound from other parts of the building and adjoining buildings sentences: - What are the fire resistance requirements for constructions separating firefighting shafts from the rest of the building or from their own components within the shaft? - What is required to provide sprinkler protection to open-plan areas in a building? - What are the sound protection requirements for the common parts of flats and similar buildings employed with workers such as cleaners and caretakers? - source_sentence: Appendix A:Key terms ..... 11Appendix B:Standards referred to ..... 12# Approved Document P:Electrical safety Dwellings## Summary0.1 This approved document gives guidance on how to comply with Part P of the Building Regulations.It contains the following sections:Section 1:Technical requirements for electrical work in dwellingsSection 2:The types of building and electrical installation within the scope of Part P, and the types of electrical work that are notifiableSection 3:The different procedures that may be followed to show that electrical work complies with Part PAppendix A:Key terms sentences: - What conditions classify a district heat network as 'under construction' based on the building regulations defined on 15 June 2022? - What are the requirements for providing reasonable access for maintaining ventilation systems in buildings? - What guidance does Approved Document P provide for complying with Part P of the Building Regulations regarding electrical work in dwellings? pipeline_tag: sentence-similarity library_name: sentence-transformers 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 model-index: - name: ModernBERT Embed base Legal Matryoshka results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 768 type: dim_768 metrics: - type: cosine_accuracy@1 value: 0.656934306569343 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8442822384428224 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8905109489051095 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9367396593673966 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.656934306569343 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.28142741281427414 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1781021897810219 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09367396593673966 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.656934306569343 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8442822384428224 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8905109489051095 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9367396593673966 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8031293036354475 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7597178774186073 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7623797955453413 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.656934306569343 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8442822384428224 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8880778588807786 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9440389294403893 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.656934306569343 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.28142741281427414 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17761557177615572 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09440389294403892 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.656934306569343 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8442822384428224 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8880778588807786 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9440389294403893 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8037305107001143 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7585679527285365 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7605016315461136 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.6472019464720195 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8369829683698297 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8880778588807786 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9440389294403893 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6472019464720195 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27899432278994324 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1776155717761557 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09440389294403892 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6472019464720195 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8369829683698297 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8880778588807786 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9440389294403893 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7994650257091209 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7527816398254354 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7549760733760151 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.610705596107056 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8126520681265207 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.851581508515815 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9294403892944039 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.610705596107056 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27088402270884027 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.170316301703163 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09294403892944038 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.610705596107056 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8126520681265207 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.851581508515815 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9294403892944039 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7725417704200617 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7222550496273127 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7253503796965786 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.5450121654501217 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7493917274939172 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.7931873479318735 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8637469586374696 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.5450121654501217 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2497972424979724 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.15863746958637467 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08637469586374696 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.5450121654501217 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7493917274939172 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.7931873479318735 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8637469586374696 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7081090686112841 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.657939404472251 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.663604050590536 name: Cosine Map@100 --- # ModernBERT Embed base Legal Matryoshka This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base). 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:** [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base) - **Maximum Sequence Length:** 8192 tokens - **Output Dimensionality:** 768 dimensions - **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': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel (1): Pooling({'word_embedding_dimension': 768, '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}) (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("calvin2258000/test2") # Run inference sentences = [ 'Appendix A:Key terms ..... 11Appendix B:Standards referred to ..... 12# Approved Document P:Electrical safety Dwellings## Summary0.1 This approved document gives guidance on how to comply with Part P of the Building Regulations.It contains the following sections:Section 1:Technical requirements for electrical work in dwellingsSection 2:The types of building and electrical installation within the scope of Part P, and the types of electrical work that are notifiableSection 3:The different procedures that may be followed to show that electrical work complies with Part PAppendix A:Key terms', 'What guidance does Approved Document P provide for complying with Part P of the Building Regulations regarding electrical work in dwellings?', "What conditions classify a district heat network as 'under construction' based on the building regulations defined on 15 June 2022?", ] 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 * Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | dim_768 | dim_512 | dim_256 | dim_128 | dim_64 | |:--------------------|:-----------|:-----------|:-----------|:-----------|:-----------| | cosine_accuracy@1 | 0.6569 | 0.6569 | 0.6472 | 0.6107 | 0.545 | | cosine_accuracy@3 | 0.8443 | 0.8443 | 0.837 | 0.8127 | 0.7494 | | cosine_accuracy@5 | 0.8905 | 0.8881 | 0.8881 | 0.8516 | 0.7932 | | cosine_accuracy@10 | 0.9367 | 0.944 | 0.944 | 0.9294 | 0.8637 | | cosine_precision@1 | 0.6569 | 0.6569 | 0.6472 | 0.6107 | 0.545 | | cosine_precision@3 | 0.2814 | 0.2814 | 0.279 | 0.2709 | 0.2498 | | cosine_precision@5 | 0.1781 | 0.1776 | 0.1776 | 0.1703 | 0.1586 | | cosine_precision@10 | 0.0937 | 0.0944 | 0.0944 | 0.0929 | 0.0864 | | cosine_recall@1 | 0.6569 | 0.6569 | 0.6472 | 0.6107 | 0.545 | | cosine_recall@3 | 0.8443 | 0.8443 | 0.837 | 0.8127 | 0.7494 | | cosine_recall@5 | 0.8905 | 0.8881 | 0.8881 | 0.8516 | 0.7932 | | cosine_recall@10 | 0.9367 | 0.944 | 0.944 | 0.9294 | 0.8637 | | **cosine_ndcg@10** | **0.8031** | **0.8037** | **0.7995** | **0.7725** | **0.7081** | | cosine_mrr@10 | 0.7597 | 0.7586 | 0.7528 | 0.7223 | 0.6579 | | cosine_map@100 | 0.7624 | 0.7605 | 0.755 | 0.7254 | 0.6636 | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 3,692 training samples * Columns: positive and anchor * Approximate statistics based on the first 1000 samples: | | positive | anchor | |:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | positive | anchor | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------| | # Section 3:Subsoil drainage3.1 The provisions which follow assume that the site of the building is not subject to general flooding (see paragraph 0.8 ) or, if it is, that appropriate steps are being taken.3.2 Where the water table can rise to within 0.25 m of the lowest floor of the building, or where surface water could enter or adversely affect the building, either the ground to be covered by the building should be drained by gravity, or other effective means of safeguarding the building should be taken.3.3 If an active subsoil drain is cut during excavation and if it passes under the building it should be:a. re-laid in pipes with sealed joints and have access points outside the building; orb. re-routed around the building; orc. re-run to another outfall (see Diagram 3).3.4 Where there is a risk that groundwater beneath or around the building could adversely affect the stability and properties of the ground, consideration should be given to site drainage or other protection (see Sec... | What measures should be taken if a building site has a water table that can rise to within 0.25 meters of the lowest floor? | | Easily accessibleEither:- a window or doorway, any part of which is within 2 m vertically of an accessible level surface such as the ground or basement level, or an access balcony, or- a window within 2 m vertically of a flat or sloping roof (with a pitch of less than $30^{\circ}$ ) that is within 3.5 m of ground level.Coupled assemblyA doorset and window that are supplied as separate self-contained frames and fixed together on site. | What criteria determine if a window or doorway is considered easily accessible in a building? | | Fuels such as bituminous coal, untreated wood or compressed paper are not smokeless or low-volatiles fuels.3.These appliances are known as 'exempted fireplaces'.2.7 For fireplaces with openings larger than $500 \mathrm{~mm} \times 550 \mathrm{~mm}$ or fireplaces exposed on two or more sides (such as a fireplace under a canopy or open on both sides of a central chimney breast) a way of showing compliance would be to provide a flue with a cross-sectional area equal to 15 per cent of the total face area of the fireplace opening(s) (see Appendix B).However, specialist advice should be sought when proposing to construct flues having an area of:a. more than 15 per cent of the total face area of the fireplace openings; or | What is the required flue cross-sectional area for fireplaces with openings larger than 500 mm x 550 mm or exposed on multiple sides? | * 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`: 10 - `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`: 10 - `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} - `tp_size`: 0 - `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`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `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 - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 | |:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:| | 1.0 | 8 | - | 0.7456 | 0.7485 | 0.7467 | 0.7117 | 0.6394 | | 1.2759 | 10 | 33.8924 | - | - | - | - | - | | 2.0 | 16 | - | 0.7781 | 0.7773 | 0.7785 | 0.7466 | 0.6837 | | 2.5517 | 20 | 10.7256 | - | - | - | - | - | | 3.0 | 24 | - | 0.7935 | 0.7870 | 0.7888 | 0.7535 | 0.7016 | | 3.8276 | 30 | 5.5408 | - | - | - | - | - | | 4.0 | 32 | - | 0.8000 | 0.7962 | 0.7969 | 0.7585 | 0.7082 | | 5.0 | 40 | 3.4556 | 0.8017 | 0.8011 | 0.7992 | 0.7644 | 0.7082 | | 6.0 | 48 | - | 0.8037 | 0.8021 | 0.7974 | 0.7692 | 0.7082 | | 6.2759 | 50 | 2.9963 | - | - | - | - | - | | 7.0 | 56 | - | 0.8025 | 0.8013 | 0.7987 | 0.7719 | 0.7072 | | 7.5517 | 60 | 3.1681 | - | - | - | - | - | | 8.0 | 64 | - | 0.8035 | 0.8024 | 0.7996 | 0.7723 | 0.7077 | | **8.8276** | **70** | **2.5551** | **0.8031** | **0.8037** | **0.7995** | **0.7725** | **0.7081** | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.12.2 - Sentence Transformers: 3.4.1 - Transformers: 4.50.0 - PyTorch: 2.6.0+cu124 - Accelerate: 1.5.2 - Datasets: 3.4.1 - Tokenizers: 0.21.1 ## 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} } ```