--- base_model: sentence-transformers/all-mpnet-base-v2 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 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:505654 - loss:MultipleNegativesRankingLoss widget: - source_sentence: 'module: stationery & printed material & services group: stationery & printed material & services supergroup: stationery & printed material & services example descriptions: munchkin crayons hween printedsheet mask 2 pk printed tape tour os silver butterfly relax with art m ab hardbacknotebook stickers p val youmeyou text heat w mandalorian a 5 nbook nediun bubble envelopes 6 pk whs pastel expan org p poll decoration 1 airtricity payasyoug' sentences: - 'retailer: groveify description: rainbow magicbooks' - 'retailer: crispcorner description: glazed k kreme' - 'retailer: vitalveg description: may held aop fl' - source_sentence: 'module: flavoured drinks carbonated cola group: drinks flavoured rtd supergroup: beverages non alcoholic example descriptions: cola w xcoke zero 15 oml pepsi 240 k coke zero 500 ml d lepsi max chry 600 coke cherry can 009500 pepsi max 500 ml tuo diet coke cf kloke zero coke zero 250 ml diet coke nin 15 cocac 3 a 250 ml coca cola 330 ml 10 px coke 125 lzero coke 250 mlreg pmpg 5 p' sentences: - 'retailer: vitalveg description: coke 240 k' - 'retailer: vitalveg description: tala silicone icing' - 'retailer: bountify description: pah antibac wood 10 l' - source_sentence: 'module: skin conditioning moisturising group: skin conditioning moisturising supergroup: personal care example descriptions: ss crmy bdy oil dove dm spa sr f m 7 nivea creme 50 carmex lime stick talc powder bo dry skn gel garnier milk bld lpblm orgnl vit a serum nv cr gran oh olay bright eye crm bio oil 2 x 200 ml nvfc srm q 10 prlbst sf aa nt crm 50 aveeno cream 500 ml' sentences: - 'retailer: wilko description: radiator m key' - 'retailer: nourify description: okf lprp tblpbl un' - 'retailer: crispcorner description: 065 each fredflo 60 biodegradable' - source_sentence: 'module: cakes gateaux ambient group: cakes gateaux ambient supergroup: food ambient example descriptions: x 20 pkmcvitiesjaffacakes 1 srn ban lunchbx js angel slices x 6 spk mr kipling frosty fancies plantastic cherry choc fl hr kipling angel slices 10 pk brompton choc brownies jschocchunknuffin loaded drip cake hobnbchoc fjack oreo muffins x 2 mr kipling victoria slices 6 pack mk kip choc rdsugar m the best brownies odby 5 choc mini' sentences: - 'retailer: flavorful description: nr choc brownies' - 'retailer: producify description: dettol srfc wipe' - 'retailer: noshify description: garden wheels plate' - source_sentence: 'module: bread ambient group: bread ambient supergroup: food ambient example descriptions: 1 war 3 toastie 400 g cc 90 varburtons bread tovis snelwrspmpkin 800 g warbutons medium bread spk giant crumpets z hovis med wht 600 g sandwich thins 5 pk warb pk crumpets mission plain tortilla 25 cm warburtons 4 protein thin bagels hovis soft wet med hovis wholemefl pataks pappadums 6 pk warb so bth disc pappajuns' sentences: - 'retailer: greenly description: pomodoro sauce' - 'retailer: crispcorner description: kingsmill 5050 medius bread 800 g' - 'retailer: vitalveg description: ready to eat prun' model-index: - name: SentenceTransformer based on sentence-transformers/all-mpnet-base-v2 results: - task: type: information-retrieval name: Information Retrieval dataset: name: sentence transformers/all mpnet base v2 type: sentence-transformers/all-mpnet-base-v2 metrics: - type: cosine_accuracy@1 value: 0.498812351543943 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.6342042755344418 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.7102137767220903 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.7838479809976246 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.498812351543943 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.21140142517814728 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.14204275534441804 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.07838479809976245 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.498812351543943 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.6342042755344418 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.7102137767220903 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.7838479809976246 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.6324346540369431 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5850111224220487 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.5910447073012788 name: Cosine Map@100 --- # SentenceTransformer based on sentence-transformers/all-mpnet-base-v2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) on the csv dataset. 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:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) - **Maximum Sequence Length:** 384 tokens - **Output Dimensionality:** 768 tokens - **Similarity Function:** Cosine Similarity - **Training Dataset:** - csv ### 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': 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() ) ``` ## 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("carnival13/all-mpnet-base-v2-modulepred") # Run inference sentences = [ 'module: bread ambient group: bread ambient supergroup: food ambient example descriptions: 1 war 3 toastie 400 g cc 90 varburtons bread tovis snelwrspmpkin 800 g warbutons medium bread spk giant crumpets z hovis med wht 600 g sandwich thins 5 pk warb pk crumpets mission plain tortilla 25 cm warburtons 4 protein thin bagels hovis soft wet med hovis wholemefl pataks pappadums 6 pk warb so bth disc pappajuns', 'retailer: crispcorner description: kingsmill 5050 medius bread 800 g', 'retailer: vitalveg description: ready to eat prun', ] 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 * Dataset: `sentence-transformers/all-mpnet-base-v2` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:----------| | cosine_accuracy@1 | 0.4988 | | cosine_accuracy@3 | 0.6342 | | cosine_accuracy@5 | 0.7102 | | cosine_accuracy@10 | 0.7838 | | cosine_precision@1 | 0.4988 | | cosine_precision@3 | 0.2114 | | cosine_precision@5 | 0.142 | | cosine_precision@10 | 0.0784 | | cosine_recall@1 | 0.4988 | | cosine_recall@3 | 0.6342 | | cosine_recall@5 | 0.7102 | | cosine_recall@10 | 0.7838 | | cosine_ndcg@10 | 0.6324 | | cosine_mrr@10 | 0.585 | | **cosine_map@100** | **0.591** | ## Training Details ### Training Dataset #### csv * Dataset: csv * Size: 505,654 training samples * Columns: query and full_doc * Approximate statistics based on the first 1000 samples: | | query | full_doc | |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | query | full_doc | |:--------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | retailer: vitalveg description: twin xira | module: chocolate single variety group: chocolate chocolate substitutes supergroup: biscuits & confectionery & snacks example descriptions: milky way twin 43 crml prtzlarum rai galaxy mnstr pipnut 34 g dark pb cup nest mnch foge p nestle smarties shar dark choc chun x 10 pk kinder bueno 1 dr oetker 72 da poppets choc offee pouch yorkie biscuit zpk haltesers truffles bog cadbury mini snowballs p terrys choc orange 3435 g galaxy fusion dark 704 100 g | | retailer: freshnosh description: mab pop sockt | module: clothing & personal accessories group: clothing & personal accessories supergroup: clothing & personal accessories example descriptions: pk blue trad ging 40 d 3 pk opaque tight t 74 green cali jogger ss animal swing yb denim stripe pump aw 21 ff vest aw 21 girls 5 pk lounge toplo sku 1 pk fleecy tight knitted pom hat pk briefs timeless double pom pomkids hat cute face twosie sku coral jersey str pun faded petrol t 32 seamfree waist c | | retailer: nourify description: bts prwn ckt swch | module: bread sandwiches filled rolls wraps group: bread fresh fixed weight supergroup: food perishable example descriptions: us chicken may hamche sw jo dbs allbtr pp st 4 js baconfree ran posh cheesy bea naturify cb swich sp eggcress f cpdfeggbacon js cheeseonion sv duck wrap reduced price takeout egg mayo sandwich 7 takeout cheeseonion s wich 2 ad leicester plough bts cheese pman 2 1 cp bacon chese s | * 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`: 4 - `per_device_eval_batch_size`: 16 - `learning_rate`: 2e-05 - `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`: 4 - `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 - `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`: 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 - `eval_use_gather_object`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | sentence-transformers/all-mpnet-base-v2_cosine_map@100 | |:------:|:----:|:-------------:|:------------------------------------------------------:| | 0.0016 | 100 | 1.6195 | 0.2567 | | 0.0032 | 200 | 1.47 | 0.3166 | | 0.0047 | 300 | 1.2703 | 0.3814 | | 0.0063 | 400 | 1.1335 | 0.4495 | | 0.0079 | 500 | 0.9942 | 0.4827 | | 0.0095 | 600 | 0.9004 | 0.5058 | | 0.0111 | 700 | 0.8838 | 0.5069 | | 0.0016 | 100 | 0.951 | 0.5197 | | 0.0032 | 200 | 0.9597 | 0.5323 | | 0.0047 | 300 | 0.9241 | 0.5406 | | 0.0063 | 400 | 0.8225 | 0.5484 | | 0.0079 | 500 | 0.7961 | 0.5568 | | 0.0095 | 600 | 0.7536 | 0.5621 | | 0.0111 | 700 | 0.7387 | 0.5623 | | 0.0127 | 800 | 0.7716 | 0.5746 | | 0.0142 | 900 | 0.7921 | 0.5651 | | 0.0158 | 1000 | 0.7744 | 0.5707 | | 0.0174 | 1100 | 0.8021 | 0.5770 | | 0.0190 | 1200 | 0.732 | 0.5756 | | 0.0206 | 1300 | 0.764 | 0.5798 | | 0.0221 | 1400 | 0.7726 | 0.5873 | | 0.0237 | 1500 | 0.6676 | 0.5921 | | 0.0253 | 1600 | 0.6851 | 0.5841 | | 0.0269 | 1700 | 0.7404 | 0.5964 | | 0.0285 | 1800 | 0.6798 | 0.5928 | | 0.0301 | 1900 | 0.6485 | 0.5753 | | 0.0316 | 2000 | 0.649 | 0.5839 | | 0.0332 | 2100 | 0.6739 | 0.5891 | | 0.0348 | 2200 | 0.6616 | 0.6045 | | 0.0364 | 2300 | 0.6287 | 0.5863 | | 0.0380 | 2400 | 0.6602 | 0.5898 | | 0.0396 | 2500 | 0.5667 | 0.5910 | ### Framework Versions - Python: 3.10.14 - Sentence Transformers: 3.1.1 - Transformers: 4.44.2 - PyTorch: 2.4.0+cu124 - Accelerate: 0.33.0 - 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} } ```