--- base_model: BAAI/bge-large-en datasets: [] language: [] library_name: sentence-transformers metrics: - cosine_accuracy - dot_accuracy - manhattan_accuracy - euclidean_accuracy - max_accuracy pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:22604 - loss:MultipleNegativesRankingLoss widget: - source_sentence: 23-0125 - Crispr mRNA Fume Hood Installations->Construction->QC Lab 1218 Fume Hood Install->Electrical - Fume Hood Power/Grounding Terminations - QC Lab sentences: - 'mat-3783s5 : 3783 Seq 5 - Material Order' - '21-1313-2.0 : Layout Drawings' - '26-0500-1.0a : Breakers (2P 20A)' - source_sentence: 23-0125 - Crispr mRNA Fume Hood Installations->Construction->QC Lab 1218 Fume Hood Install->Electrical - Fume Hood Power/Grounding Terminations - QC Lab sentences: - '26-0500-1.3 : Cabling / Wiring' - '26-0500-1.0a : Breakers (2P 20A)' - '23-2000-1.1 : HWR and HWS Pipe, Valves and Fittings' - source_sentence: 3783 UC Davis (Northern Cal - Jon Sanguinetti)->Seq 5-P-3783 sentences: - 'mat-3783s8 : 3783 Seq 8 - Material Order' - 'mat-3783s5 : 3783 Seq 5 - Material Order' - 'mat-3786s18 : 3786 Seq 18 - Material Order' - source_sentence: 3786 Rady (Pacific - JD Hudson)->Seq 18-P-3786 sentences: - '26-0500-1.0a : Breakers (2P 20A)' - 'dwg-3786s18 : 3786 Seq 18 - Drawings' - '23-7000-4.0b : EAV-91623' - source_sentence: 3783 UC Davis (Northern Cal - Jon Sanguinetti)->Seq 18-P-3783 sentences: - 'mat-3783s5 : 3783 Seq 5 - Material Order' - 'dwg-3783s8 : 3783 Seq 8 - Drawings' - 'dwg-3783s18 : 3783 Seq 18 - Drawings' model-index: - name: SentenceTransformer based on BAAI/bge-large-en results: - task: type: triplet name: Triplet dataset: name: custom bge dev type: custom-bge-dev metrics: - type: cosine_accuracy value: 0.9838187702265372 name: Cosine Accuracy - type: dot_accuracy value: 0.016181229773462782 name: Dot Accuracy - type: manhattan_accuracy value: 0.9838187702265372 name: Manhattan Accuracy - type: euclidean_accuracy value: 0.9838187702265372 name: Euclidean Accuracy - type: max_accuracy value: 0.9838187702265372 name: Max Accuracy - task: type: triplet name: Triplet dataset: name: custom bge test type: custom-bge-test metrics: - type: cosine_accuracy value: 0.9838187702265372 name: Cosine Accuracy - type: dot_accuracy value: 0.016181229773462782 name: Dot Accuracy - type: manhattan_accuracy value: 0.9838187702265372 name: Manhattan Accuracy - type: euclidean_accuracy value: 0.9838187702265372 name: Euclidean Accuracy - type: max_accuracy value: 0.9838187702265372 name: Max Accuracy --- # SentenceTransformer based on BAAI/bge-large-en This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-large-en](https://huggingface.co./BAAI/bge-large-en). It maps sentences & paragraphs to a 1024-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-large-en](https://huggingface.co./BAAI/bge-large-en) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 1024 tokens - **Similarity Function:** Cosine Similarity ### 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': 1024, '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("rnbokade/custom-bge") # Run inference sentences = [ '3783 UC Davis (Northern Cal - Jon Sanguinetti)->Seq 18-P-3783', 'dwg-3783s18 : 3783 Seq 18 - Drawings', 'mat-3783s5 : 3783 Seq 5 - Material Order', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Triplet * Dataset: `custom-bge-dev` * Evaluated with [TripletEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) | Metric | Value | |:-------------------|:-----------| | cosine_accuracy | 0.9838 | | dot_accuracy | 0.0162 | | manhattan_accuracy | 0.9838 | | euclidean_accuracy | 0.9838 | | **max_accuracy** | **0.9838** | #### Triplet * Dataset: `custom-bge-test` * Evaluated with [TripletEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) | Metric | Value | |:-------------------|:-----------| | cosine_accuracy | 0.9838 | | dot_accuracy | 0.0162 | | manhattan_accuracy | 0.9838 | | euclidean_accuracy | 0.9838 | | **max_accuracy** | **0.9838** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 22,604 training samples * Columns: anchor, positive, and negative * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | anchor | positive | negative | |:-------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------|:--------------------------------------------------------| | MOD 1- Metal Decking - Floor
Stud Wall Panels
Floor Sheathing (Megaboard) Layout of Dirtt Frame Centerlines | EW1001-125 : Door Slabs / Frames / Hardware | dwg-3783s16 : 3783 Seq 16 - Drawings | | MOD 1- Metal Decking - Floor
Stud Wall Panels
Floor Sheathing (Megaboard) Layout of Dirtt Frame Centerlines | EW1001-125 : Door Slabs / Frames / Hardware | mat-3783s16 : 3783 Seq 16 - Material Order | | MOD 1- Metal Decking - Floor
Stud Wall Panels
Floor Sheathing (Megaboard) Layout of Dirtt Frame Centerlines | EW1001-125 : Door Slabs / Frames / Hardware | dwg-3786s292 : 3786 Seq 292 - Drawings | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 618 evaluation samples * Columns: anchor, positive, and negative * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | anchor | positive | negative | |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------|:--------------------------------------------------------| | 23-0125 - Crispr mRNA Fume Hood Installations->Construction->QC Lab 1218 Fume Hood Install->Electrical - Fume Hood Power/Grounding Terminations - QC Lab | 26-0500-1.0 : Breakers (3P 20A) | dwg-3786s17 : 3786 Seq 17 - Drawings | | 23-0125 - Crispr mRNA Fume Hood Installations->Construction->QC Lab 1218 Fume Hood Install->Electrical - Fume Hood Power/Grounding Terminations - QC Lab | 26-0500-1.0 : Breakers (3P 20A) | mat-3786s17 : 3786 Seq 17 - Material Order | | 23-0125 - Crispr mRNA Fume Hood Installations->Construction->QC Lab 1218 Fume Hood Install->Electrical - Fume Hood Power/Grounding Terminations - QC Lab | 26-0500-1.0 : Breakers (3P 20A) | 09-9000-2.0 : Paint and Coatings | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `fp16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `learning_rate`: 5e-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`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `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`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `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`: False - `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 - `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 | loss | custom-bge-dev_max_accuracy | custom-bge-test_max_accuracy | |:------:|:----:|:-------------:|:------:|:---------------------------:|:----------------------------:| | 0 | 0 | - | - | 0.8463 | - | | 0.0708 | 100 | 0.5651 | 0.6065 | 0.9919 | - | | 0.1415 | 200 | 0.168 | 0.4217 | 0.9935 | - | | 0.2123 | 300 | 0.0499 | 0.6747 | 0.9951 | - | | 0.2831 | 400 | 0.2205 | 0.8112 | 0.9951 | - | | 0.3539 | 500 | 0.1167 | 0.7040 | 0.9903 | - | | 0.4246 | 600 | 0.0968 | 0.7364 | 0.9822 | - | | 0.4954 | 700 | 0.1704 | 0.5540 | 0.9968 | - | | 0.5662 | 800 | 0.1104 | 0.7266 | 0.9951 | - | | 0.6369 | 900 | 0.1698 | 1.1020 | 0.9725 | - | | 0.7077 | 1000 | 0.1077 | 0.9028 | 0.9790 | - | | 0.7785 | 1100 | 0.1667 | 0.8478 | 0.9757 | - | | 0.8493 | 1200 | 0.0707 | 0.7629 | 0.9887 | - | | 0.9200 | 1300 | 0.0299 | 0.8024 | 0.9871 | - | | 0.9908 | 1400 | 0.0005 | 0.8161 | 0.9838 | - | | 1.0 | 1413 | - | - | - | 0.9838 | ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.1 - Transformers: 4.42.4 - PyTorch: 2.3.1+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", } ``` #### 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} } ```