--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:3820 - loss:MultipleNegativesRankingLoss widget: - source_sentence: samsung ms23h3125ak/ms23h3125ak sentences: - Canon EOS M50 + 15-45mm IS STM - Bosch KIV32X23GB Integrated - Indesit DIF04B1 Integrated - Samsung MS23H3125AK Black - Samsung RB29FWRNDBC Black - Hisense RQ560N4WC1 - Samsung UE32M5520 - Nikon CoolPix A10 - Hotpoint RPD10457JKK - HP Intel Xeon X5670 2.93GHz Socket 1366 3200MHz bus Upgrade Tray - Indesit DFG15B1S Silver - Samsung WW10M86DQOO - Bosch SMV46MX00G Integrated - LG 49SK8100PLA - Nikon CoolPix W300 - AMD Ryzen 3 1300X 3.5GHz Box - LG OLED65B8PLA - Samsung Galaxy J5 SM-J530 - LG 65UK6500PLA - Siemens WM14T391GB - Apple iPhone SE 32GB - source_sentence: lg oled65c8pla sentences: - Beko LCSM1545W White - Bosch KAN90VI20G Stainless Steel - Canon PowerShot SX60 HS - Hotpoint WMAQF621P - Apple iPhone 7 Plus 32GB - Hotpoint FFU4DK Black - Fujifilm Finepix XP130 - Bosch WAN24108GB - LG OLED65E8PLA - Intel Core i7-8700K 3.7GHz Box - Fujifilm X-Pro2 - LG OLED65C8PLA - Samsung UE55NU8000 - LG 49LK5900PLA - Apple iPhone 8 64GB - Samsung UE65NU7100 - AEG L6FBG942R - AMD Ryzen 7 1700 3GHz Box - Panasonic TX-49FX750B - Bosch WKD28351GB - Bosch GUD15A50GB Integrated - source_sentence: 15.748 cm 6.2 2960 x 1440 samoled octa core 2.3ghz quad 1.7gh sentences: - Apple iPhone SE 32GB - Apple iPhone X 64GB - LG 55SK9500PLA - Sony Cyber-shot DSC-WX500 - Samsung Galaxy A5 SM-A520F - Apple iPhone 8 Plus 64GB - Indesit IWDD7123 - Bosch SMS67MW01G White - Bosch KGV33XW30G White - Samsung WW80K5413UW - AMD Ryzen 3 1300X 3.5GHz Box - Bosch WAW28750GB - Samsung Galaxy S8+ 64GB - Bosch KGN39VW35G White - Intel Core i7-7700K 4.2GHz Box - Hotpoint RZAAV22P White - Samsung UE49NU8000 - HP AMD Opteron 6276 2.3GHz Upgrade Tray - Praktica Luxmedia Z250 - Hotpoint HFC2B19SV White - Hisense RB385N4EW1 White - source_sentence: boxed processor amd ryzen 3 1200 4 x 3.1 ghz quad sentences: - Bosch KGN36HI32 Stainless Steel - Bosch SMS24AW01G White - Hotpoint WDAL8640P - Doro 6050 - Samsung QE55Q7FN - AMD Ryzen 3 1200 3.1GHz Box - Samsung UE55NU7500 - Huawei Honor 10 128GB Dual SIM - Sony Xperia L1 - Hotpoint FFU4DK Black - Hoover DXOC 68C3B - Sony Xperia XA1 - Nikon D7200 + 18-105mm VR - HP Intel Xeon DP E5640 2.66GHz Socket 1366 1066MHz bus Upgrade Tray - Samsung UE49NU8000 - Panasonic Lumix DMC-FT30 - Hotpoint FDL 9640K UK - Apple iPhone 6S Plus 128GB - Nikon D5600 + AF-P 18-55mm VR - HP AMD Opteron 6238 2.6GHz Upgrade Tray - Apple iPhone SE 32GB - source_sentence: lg 49uk6300plb/49uk6300plb sentences: - Bosch KIR24V20GB Integrated - Bosch WAWH8660GB - Intel Core i5-7600K 3.80GHz Box - Sony Bravia KD-65AF8 - Samsung RL4362FBASL Stainless Steel - Bosch SMI50C15GB Silver - Apple iPhone XS Max 256GB - Fujifilm X-T100 + XC 15-45/f3.5-5.6 OIS PZ - Bosch KGN36VW35G White - Samsung WW70K5410UW - Samsung Galaxy J6 - LG 49UK6300PLB - Doro Secure 580 - Sony Xperia XZ1 Compact - Bosch SMV50C10GB Integrated - Bosch KGN34VB35G Black - Panasonic NN-E27JWMBPQ White - Samsung WW10M86DQOA/EU - LG 55SK9500PLA - Samsung QE65Q8DN - Canon EOS 80D 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: SentenceTransformer results: - task: type: information-retrieval name: Information Retrieval dataset: name: Product Category Retrieval Test type: Product-Category-Retrieval-Test metrics: - type: cosine_accuracy@1 value: 0.8085774058577406 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9476987447698745 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9644351464435147 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9769874476987448 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.8085774058577406 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3158995815899582 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.19288702928870294 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09769874476987449 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.8085774058577406 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.9476987447698745 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9644351464435147 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9769874476987448 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9041917131034228 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.879607906621505 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.8805000617705705 name: Cosine Map@100 --- # SentenceTransformer This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 512-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 - **Maximum Sequence Length:** 384 tokens - **Output Dimensionality:** 512 dimensions - **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): SentenceTransformer( (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel (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() ) (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): Dense({'in_features': 768, 'out_features': 512, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) ) ``` ## 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("llmvetter/embedding_finetune") # Run inference sentences = [ 'lg 49uk6300plb/49uk6300plb', 'LG 49UK6300PLB', 'Samsung Galaxy J6', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 512] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Information Retrieval * Dataset: `Product-Category-Retrieval-Test` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.8086 | | cosine_accuracy@3 | 0.9477 | | cosine_accuracy@5 | 0.9644 | | cosine_accuracy@10 | 0.977 | | cosine_precision@1 | 0.8086 | | cosine_precision@3 | 0.3159 | | cosine_precision@5 | 0.1929 | | cosine_precision@10 | 0.0977 | | cosine_recall@1 | 0.8086 | | cosine_recall@3 | 0.9477 | | cosine_recall@5 | 0.9644 | | cosine_recall@10 | 0.977 | | **cosine_ndcg@10** | **0.9042** | | cosine_mrr@10 | 0.8796 | | cosine_map@100 | 0.8805 | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 3,820 training samples * Columns: sentence_0, sentence_1, sentence_2, sentence_3, sentence_4, sentence_5, sentence_6, sentence_7, sentence_8, sentence_9, sentence_10, sentence_11, sentence_12, sentence_13, sentence_14, sentence_15, sentence_16, sentence_17, sentence_18, sentence_19, sentence_20, and sentence_21 * Approximate statistics based on the first 1000 samples: | | sentence_0 | sentence_1 | sentence_2 | sentence_3 | sentence_4 | sentence_5 | sentence_6 | sentence_7 | sentence_8 | sentence_9 | sentence_10 | sentence_11 | sentence_12 | sentence_13 | sentence_14 | sentence_15 | sentence_16 | sentence_17 | sentence_18 | sentence_19 | sentence_20 | sentence_21 | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | | details | | | | | | | | | | | | | | | | | | | | | | | * Samples: | sentence_0 | sentence_1 | sentence_2 | sentence_3 | sentence_4 | sentence_5 | sentence_6 | sentence_7 | sentence_8 | sentence_9 | sentence_10 | sentence_11 | sentence_12 | sentence_13 | sentence_14 | sentence_15 | sentence_16 | sentence_17 | sentence_18 | sentence_19 | sentence_20 | sentence_21 | |:---------------------------------------------------------------------|:----------------------------------------|:---------------------------------------------|:-------------------------------------|:-------------------------------------|:--------------------------------------|:----------------------------------------------|:----------------------------------|:---------------------------------|:----------------------------------------------|:-----------------------------------------------------------------------------|:---------------------------------------------|:------------------------------------|:--------------------------------------------|:---------------------------------------------|:----------------------------------------|:-------------------------------------------------|:-------------------------------|:------------------------------------------|:---------------------------------------|:--------------------------------------------------------------------------------|:--------------------------------------------------------------------------------| | sony kd49xf8505bu 49 4k ultra hd tv | Sony Bravia KD-49XF8505 | Intel Core i7-8700K 3.7GHz Box | Bosch WAN24100GB | AMD FX-6300 3.5GHz Box | Bosch WIW28500GB | Bosch KGN36VL35G Stainless Steel | Indesit XWDE751480XS | CAT S41 Dual SIM | Sony Xperia XA1 Ultra 32GB | Samsung Galaxy J6 | Samsung QE55Q7FN | Bosch KGN39VW35G White | Intel Core i5 7400 3.0GHz Box | Neff C17UR02N0B Stainless Steel | Samsung RR39M7340SA Silver | Samsung RB41J7255SR Stainless Steel | Hoover DXOC 68C3B | Canon PowerShot SX730 HS | Samsung RR39M7340BC Black | Praktica Luxmedia WP240 | HP Intel Xeon DP E5506 2.13GHz Socket 1366 800MHz bus Upgrade Tray | | doro 8040 4g sim free mobile phone black | Doro 8040 | Bosch HMT75M551 Stainless Steel | Bosch SMI50C15GB Silver | Samsung WW90K5413UX | Panasonic Lumix DMC-TZ70 | Sony KD-49XF7073 | Nikon CoolPix W100 | Samsung WD90J6A10AW | Bosch CFA634GS1B Stainless Steel | HP AMD Opteron 8425 HE 2.1GHz Socket F 4800MHz bus Upgrade Tray | Canon EOS 800D + 18-55mm IS STM | Samsung UE50NU7400 | Apple iPhone 6S 128GB | Samsung RS52N3313SA/EU Graphite | Bosch WAW325H0GB | Sony Bravia KD-55AF8 | Sony Alpha 6500 | Doro 5030 | LG GSL761WBXV Black | Bosch SMS67MW00G White | AEG L6FBG942R | | fridgemaster muz4965 undercounter freezer white a rated | Fridgemaster MUZ4965 White | Samsung UE49NU7100 | Nikon CoolPix A10 | Samsung UE55NU7100 | Samsung QE55Q7FN | Bosch KGN49XL30G Stainless Steel | Samsung UE49NU7500 | LG 55UK6300PLB | Hoover DXOC 68C3B | Panasonic Lumix DMC-FZ2000 | Panasonic Lumix DMC-TZ80 | Bosch WKD28541GB | Apple iPhone 6 32GB | Sony Bravia KDL-32WE613 | Lec TF50152W White | Bosch KGV36VW32G White | Bosch WAYH8790GB | Samsung RS68N8240B1/EU Black | Sony Xperia XZ1 | HP Intel Xeon DP E5506 2.13GHz Socket 1366 800MHz bus Upgrade Tray | Sharp R372WM White | * 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 - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `num_train_epochs`: 8 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_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 - `num_train_epochs`: 8 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `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`: False - `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`: 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`: batch_sampler - `multi_dataset_batch_sampler`: round_robin
### Training Logs | Epoch | Step | Training Loss | Product-Category-Retrieval-Test_cosine_ndcg@10 | |:------:|:----:|:-------------:|:----------------------------------------------:| | 1.0 | 120 | - | 0.7406 | | 2.0 | 240 | - | 0.8437 | | 3.0 | 360 | - | 0.8756 | | 4.0 | 480 | - | 0.8875 | | 4.1667 | 500 | 2.5302 | - | | 5.0 | 600 | - | 0.8963 | | 6.0 | 720 | - | 0.9015 | | 7.0 | 840 | - | 0.9042 | ### Framework Versions - Python: 3.11.10 - Sentence Transformers: 3.3.1 - Transformers: 4.47.1 - PyTorch: 2.5.1+cu124 - Accelerate: 1.2.1 - Datasets: 3.2.0 - Tokenizers: 0.21.0 ## 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", } ``` #### 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} } ```