--- language: - vi license: apache-2.0 tags: - sentence-transformers - sentence-similarity - feature-extraction - dense - generated_from_trainer - dataset_size:6066 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss base_model: splendor1811/BGE-base-banking-ONE-v0106 widget: - source_sentence: HUYNHDEANHKHOA COMPANY LIMITED sentences: - SHYH JIUH INDUSTRIAL CO.,LTD - KENG HIN ENGINEERING CO - CTTNHHTHIETKE MYTHUAT VA TINHOC HUYNHDEANHKHOA - source_sentence: YUEQING RONGSHENG ELECTRICAL APPLIANCES LTD. sentences: - RAUCH FRUCHTSÄFTE GMBH & CO OG - YUEQING RONGSHENG INTRODUCED ELECTRICAL APPLIANCES CO., LTD - BACH MY TRADING, SERVICES AND CONSTRUCTION COMPANY LIMITED - source_sentence: ZHENFA TEXTILE COMPANY, LIMITED sentences: - DATANI LOGISTICS COMPANY LIMITED - Quan Pham Electrical Equipment Co., Ltd. - ZHENFA TEXTILE CO., LIMITED - source_sentence: CONG TY TNHH KY THUAT VSI sentences: - CTCAD SERVICES TRADING COMPANY LIMITED - CHAKTOMUK RESOURCES SUPPLY IMPORT EXPORT CO.,LTD - VSI ENGINEERING COMPANY LIMITED - source_sentence: VIET ANH DUONG CO., LTD sentences: - ROCK GRANITES - CTY TNHH VIET ANH DUONG - SOVAN SEUNGDEN CO.,LTD 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: BGE-mapping-tool results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 1024 type: dim_1024 metrics: - type: cosine_accuracy@1 value: 0.844213649851632 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9169139465875371 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9540059347181009 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.983679525222552 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.844213649851632 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.8209693372898119 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.8172106824925816 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.7956973293768547 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.04068450507619647 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.11841496782149599 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.1962584728163363 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.38186014447142047 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8055555734311861 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8893975790118221 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.8042214818897099 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 768 type: dim_768 metrics: - type: cosine_accuracy@1 value: 0.8501483679525222 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9124629080118695 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9525222551928784 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.983679525222552 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.8501483679525222 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.8199802176063304 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.8160237388724035 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.7958456973293768 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.040903524434681704 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.1182842627204645 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.1959109963561002 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.38199438214275006 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8060214331494838 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8915365503273512 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.8045717264189338 name: Cosine Map@100 --- # BGE-mapping-tool This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [splendor1811/BGE-base-banking-ONE-v0106](https://huggingface.co/splendor1811/BGE-base-banking-ONE-v0106) on the json dataset. 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:** [splendor1811/BGE-base-banking-ONE-v0106](https://huggingface.co/splendor1811/BGE-base-banking-ONE-v0106) - **Maximum Sequence Length:** 8192 tokens - **Output Dimensionality:** 1024 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - json - **Language:** vi - **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, 'architecture': 'XLMRobertaModel'}) (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("splendor1811/BGE-mapping-tool_epoch3") # Run inference sentences = [ 'VIET ANH DUONG CO., LTD', 'CTY TNHH VIET ANH DUONG', 'ROCK GRANITES', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities) # tensor([[ 1.0000, 0.9738, -0.0072], # [ 0.9738, 1.0000, 0.0011], # [-0.0072, 0.0011, 1.0000]]) ``` ## Evaluation ### Metrics #### Information Retrieval * Dataset: `dim_1024` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: ```json { "truncate_dim": 1024 } ``` | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.8442 | | cosine_accuracy@3 | 0.9169 | | cosine_accuracy@5 | 0.954 | | cosine_accuracy@10 | 0.9837 | | cosine_precision@1 | 0.8442 | | cosine_precision@3 | 0.821 | | cosine_precision@5 | 0.8172 | | cosine_precision@10 | 0.7957 | | cosine_recall@1 | 0.0407 | | cosine_recall@3 | 0.1184 | | cosine_recall@5 | 0.1963 | | cosine_recall@10 | 0.3819 | | **cosine_ndcg@10** | **0.8056** | | cosine_mrr@10 | 0.8894 | | cosine_map@100 | 0.8042 | #### Information Retrieval * Dataset: `dim_768` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: ```json { "truncate_dim": 768 } ``` | Metric | Value | |:--------------------|:----------| | cosine_accuracy@1 | 0.8501 | | cosine_accuracy@3 | 0.9125 | | cosine_accuracy@5 | 0.9525 | | cosine_accuracy@10 | 0.9837 | | cosine_precision@1 | 0.8501 | | cosine_precision@3 | 0.82 | | cosine_precision@5 | 0.816 | | cosine_precision@10 | 0.7958 | | cosine_recall@1 | 0.0409 | | cosine_recall@3 | 0.1183 | | cosine_recall@5 | 0.1959 | | cosine_recall@10 | 0.382 | | **cosine_ndcg@10** | **0.806** | | cosine_mrr@10 | 0.8915 | | cosine_map@100 | 0.8046 | ## Training Details ### Training Dataset #### json * Dataset: json * Size: 6,066 training samples * Columns: anchor and positive * Approximate statistics based on the first 1000 samples: | | anchor | positive | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | anchor | positive | |:-------------------------------------------------------------|:----------------------------------------------------------| | ZHONGSHAN CHINHAO MOLD HARDWARE COMPANY LIMITED | ZHONGSHAN CHINHAO MOLD HARDWARE CO., LTD. | | Changshu Longte International Trade Co., Ltd | CHANGSHU LONGTE INTERNATIONAL TRADE CO., LTD | | ACT CHEMICAL AND THERAPEUTIC LABORATORIES SARL | MEDICAL SUPPLIES PHARMACEUTICALS & EQUIPMENT | * Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 768 ], "matryoshka_weights": [ 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `gradient_accumulation_steps`: 8 - `learning_rate`: 2e-05 - `num_train_epochs`: 5 - `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`: 8 - `per_device_eval_batch_size`: 8 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 8 - `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`: 5 - `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} - `parallelism_config`: 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 - `hub_revision`: None - `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 - `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 - `liger_kernel_config`: None - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional - `router_mapping`: {} - `learning_rate_mapping`: {}
### Training Logs | Epoch | Step | Training Loss | dim_1024_cosine_ndcg@10 | dim_768_cosine_ndcg@10 | |:-------:|:-------:|:-------------:|:-----------------------:|:----------------------:| | 0.1054 | 10 | 0.4225 | - | - | | 0.2108 | 20 | 0.2415 | - | - | | 0.3162 | 30 | 0.1252 | - | - | | 0.4216 | 40 | 0.0765 | - | - | | 0.5270 | 50 | 0.0573 | - | - | | 0.6324 | 60 | 0.0345 | - | - | | 0.7378 | 70 | 0.0448 | - | - | | 0.8432 | 80 | 0.0355 | - | - | | 0.9486 | 90 | 0.0996 | - | - | | 1.0 | 95 | - | 0.7994 | 0.7994 | | 1.0527 | 100 | 0.0194 | - | - | | 1.1581 | 110 | 0.0418 | - | - | | 1.2635 | 120 | 0.0339 | - | - | | 1.3689 | 130 | 0.0427 | - | - | | 1.4743 | 140 | 0.0339 | - | - | | 1.5797 | 150 | 0.0333 | - | - | | 1.6851 | 160 | 0.0396 | - | - | | 1.7905 | 170 | 0.0877 | - | - | | 1.8959 | 180 | 0.0608 | - | - | | 2.0 | 190 | 0.0352 | 0.8037 | 0.8031 | | 2.1054 | 200 | 0.023 | - | - | | 2.2108 | 210 | 0.0638 | - | - | | 2.3162 | 220 | 0.0401 | - | - | | 2.4216 | 230 | 0.0274 | - | - | | 2.5270 | 240 | 0.0405 | - | - | | 2.6324 | 250 | 0.0305 | - | - | | 2.7378 | 260 | 0.0414 | - | - | | 2.8432 | 270 | 0.0178 | - | - | | 2.9486 | 280 | 0.0535 | - | - | | 3.0 | 285 | - | 0.8008 | 0.8012 | | 3.0527 | 290 | 0.0629 | - | - | | 3.1581 | 300 | 0.0283 | - | - | | 3.2635 | 310 | 0.0567 | - | - | | 3.3689 | 320 | 0.0167 | - | - | | 3.4743 | 330 | 0.0349 | - | - | | 3.5797 | 340 | 0.053 | - | - | | 3.6851 | 350 | 0.0517 | - | - | | 3.7905 | 360 | 0.0603 | - | - | | 3.8959 | 370 | 0.0323 | - | - | | 4.0 | 380 | 0.0229 | 0.8042 | 0.8055 | | 4.1054 | 390 | 0.0476 | - | - | | 4.2108 | 400 | 0.06 | - | - | | 4.3162 | 410 | 0.0412 | - | - | | 4.4216 | 420 | 0.0553 | - | - | | 4.5270 | 430 | 0.0446 | - | - | | 4.6324 | 440 | 0.016 | - | - | | 4.7378 | 450 | 0.0302 | - | - | | 4.8432 | 460 | 0.0223 | - | - | | 4.9486 | 470 | 0.0649 | - | - | | **5.0** | **475** | **-** | **0.8056** | **0.806** | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.11.13 - Sentence Transformers: 5.1.0 - Transformers: 4.56.1 - PyTorch: 2.6.0+cu124 - Accelerate: 1.8.1 - Datasets: 3.6.0 - Tokenizers: 0.22.0 ## 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} } ```