--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:10000 - loss:TripletLoss base_model: sentence-transformers/all-mpnet-base-v2 widget: - source_sentence: 'Database: trna-n6-adenosine-threonylcarbamoyltransferase-tsad | Table: trna-n6-adenosine-threonylcarbamoyltransferase-tsad_natural-product | Sample: ...' sentences: - 'Database: trna-n6-adenosine-threonylcarbamoyltransferase-tsad | Table: trna-n6-adenosine-threonylcarbamoyltransferase-tsad_trna-n6-adenosine-threonylcarbamoyltransferase-tsad | Sample: ...' - 'Database: oxidoreductase-alpha-molybdopterin-subunit | Table: oxidoreductase-alpha-molybdopterin-subunit_oxidoreductase-alpha-molybdopterin-subunit | Sample: ...' - 'Database: protein-of-unknown-function-duf3561 | Table: protein-of-unknown-function-duf3561_encoded-by | Sample: ...' - source_sentence: 'Database: maastricht-formation | Table: maastricht-formation_eponym | Sample: ...' sentences: - 'Database: maastricht-formation | Table: maastricht-formation_eponym | Sample: ...' - 'Database: uncharacterised-protein-yqey-aim41 | Table: uncharacterised-protein-yqey-aim41_uncharacterised-protein-yqey-aim41 | Sample: ...' - 'Database: 3-dehydro-bile-acid-delta46-reductase-like | Table: 3-dehydro-bile-acid-delta46-reductase-like_encoded-by | Sample: ...' - source_sentence: 'Database: hexokinase-subgroup | Table: hexokinase-subgroup_encoded-by | Sample: ...' sentences: - 'Database: fictional-energy | Table: fictional-energy_fictional-energy | Sample: ...' - 'Database: co-dehydrogenase-flavoprotein-c-terminal-domain-superfamily | Table: co-dehydrogenase-flavoprotein-c-terminal-domain-superfamily_co-dehydrogenase-flavoprotein-c-terminal-domain-superfamily | Sample: ...' - 'Database: hexokinase-subgroup | Table: hexokinase-subgroup_chromosome | Sample: ...' - source_sentence: 'Database: 2010-fifa-world-cup-qualification-uefa-group-1 | Table: 2010-fifa-world-cup-qualification-uefa-group-1_occupant | Sample: ...' sentences: - 'Database: 2010-fifa-world-cup-qualification-uefa-group-1 | Table: 2010-fifa-world-cup-qualification-uefa-group-1_occupant | Sample: ...' - 'Database: rna-polymerase-beta-subunit-conserved-site-protein-family | Table: rna-polymerase-beta-subunit-conserved-site-protein-family_rna-polymerase-beta-subunit-conserved-site-protein-family | Sample: ...' - 'Database: anaphase-promoting-complex-subunit-4-wd40-domain-protein-family | Table: anaphase-promoting-complex-subunit-4-wd40-domain-protein-family_natural-product | Sample: ...' - source_sentence: 'Database: bothy | Table: bothy_capital-city | Sample: ...' sentences: - 'Database: bothy | Table: bothy_administrative-centre | Sample: ...' - 'Database: provincial-museum-in-finland | Table: provincial-museum-in-finland_jurisdiction | Sample: ...' - 'Database: saicar-synthetase-conserved-site-protein-family | Table: saicar-synthetase-conserved-site-protein-family_encoded-by | Sample: ...' pipeline_tag: sentence-similarity library_name: sentence-transformers --- # 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). 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 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): 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("sentence_transformers_model_id") # Run inference sentences = [ 'Database: bothy | Table: bothy_capital-city | Sample: ...', 'Database: bothy | Table: bothy_administrative-centre | Sample: ...', 'Database: saicar-synthetase-conserved-site-protein-family | Table: saicar-synthetase-conserved-site-protein-family_encoded-by | Sample: ...', ] 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] ``` ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 10,000 training samples * Columns: sentence_0, sentence_1, and sentence_2 * Approximate statistics based on the first 1000 samples: | | sentence_0 | sentence_1 | sentence_2 | |:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | sentence_0 | sentence_1 | sentence_2 | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Database: protein-of-unknown-function-duf2635 | Table: protein-of-unknown-function-duf2635_encodes | Sample: ... | Database: protein-of-unknown-function-duf2635 | Table: protein-of-unknown-function-duf2635_protein-of-unknown-function-duf2635 | Sample: ... | Database: extinct-volcano | Table: extinct-volcano_country | Sample: ... | | Database: tetrapyrrole-biosynthesis-glutamate-1-semialdehyde-aminotransferase | Table: tetrapyrrole-biosynthesis-glutamate-1-semialdehyde-aminotransferase_natural-product | Sample: ... | Database: tetrapyrrole-biosynthesis-glutamate-1-semialdehyde-aminotransferase | Table: tetrapyrrole-biosynthesis-glutamate-1-semialdehyde-aminotransferase_tetrapyrrole-biosynthesis-glutamate-1-semialdehyde-aminotransferase | Sample: ... | Database: pili-assembly-chaperone-conserved-site-protein-family | Table: pili-assembly-chaperone-conserved-site-protein-family_encoded-by | Sample: ... | | Database: series-of-images | Table: series-of-images_series-of-images | Sample: ... | Database: series-of-images | Table: series-of-images_topic | Sample: ... | Database: claretian-martyrs-of-barbastro | Table: claretian-martyrs-of-barbastro_claretian-martyrs-of-barbastro | Sample: ... | * Loss: [TripletLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters: ```json { "distance_metric": "TripletDistanceMetric.EUCLIDEAN", "triplet_margin": 5 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `fp16`: True - `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`: 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 - `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`: 3 - `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`: 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`: 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 | |:-----:|:----:|:-------------:| | 0.8 | 500 | 4.0418 | | 1.6 | 1000 | 3.8665 | | 2.4 | 1500 | 3.8205 | ### Framework Versions - Python: 3.10.15 - Sentence Transformers: 3.4.1 - Transformers: 4.49.0 - PyTorch: 2.6.0+cu118 - Accelerate: 0.29.3 - 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", } ``` #### TripletLoss ```bibtex @misc{hermans2017defense, title={In Defense of the Triplet Loss for Person Re-Identification}, author={Alexander Hermans and Lucas Beyer and Bastian Leibe}, year={2017}, eprint={1703.07737}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```