--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:40906 - loss:MatryoshkaLoss - loss:MegaBatchMarginLoss widget: - source_sentence: >- One of three laminate structures that form the spindle pole body; the inner plaque is in the nucleus. sentences: - >- maturation of SSU-rRNA from tetracistronic rRNA transcript (SSU-rRNA, 5.8S rRNA, 2S rRNA, LSU-rRNA) - leukotriene receptor activity - inner plaque of spindle pole body - source_sentence: >- The covalent attachment of a myristoyl group to the N-terminal amino acid residue of a protein. sentences: - MHC class I protein complex assembly - N-terminal protein myristoylation - neurotrophin receptor activity - source_sentence: >- The inner, i.e. lumen-facing, lipid bilayer of the plastid envelope; also faces the plastid stroma. sentences: - plastid inner membrane - neuron migration involved in retrograde extension - stomatal complex morphogenesis - source_sentence: >- Initiation of a region of tissue in a plant that is composed of one or more undifferentiated cells capable of undergoing mitosis and differentiation, thereby effecting growth and development of a plant by giving rise to more meristem or specialized tissue. sentences: - meristem initiation - polytene chromosome - cardiac ventricle development - source_sentence: >- The sex chromosome present in both sexes of species in which the male is the heterogametic sex. Two copies of the X chromosome are present in each somatic cell of females and one copy is present in males. sentences: - establishment of cell polarity involved in gastrulation cell migration - X chromosome - somatic diversification of immune receptors by N region addition pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - src2trg_accuracy - trg2src_accuracy - mean_accuracy model-index: - name: SentenceTransformer results: - task: type: translation name: Translation dataset: name: Unknown type: unknown metrics: - type: src2trg_accuracy value: 0.00015186028853454822 name: Src2Trg Accuracy - type: trg2src_accuracy value: 0 name: Trg2Src Accuracy - type: mean_accuracy value: 0.00007593014426727411 name: Mean Accuracy license: apache-2.0 datasets: - NothingMuch/GO-Terms language: - en base_model: - Snowflake/snowflake-arctic-embed-m-v1.5 --- # SentenceTransformer This is a [sentence-transformers](https://www.SBERT.net) model trained on the parquet dataset. It maps sentences & paragraphs to a 128-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:** 512 tokens - **Output Dimensionality:** 128 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - parquet ### 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': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, '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("GO-Term-Embeddings") # Run inference sentences = [ 'The sex chromosome present in both sexes of species in which the male is the heterogametic sex. Two copies of the X chromosome are present in each somatic cell of females and one copy is present in males.', 'X chromosome', 'somatic diversification of immune receptors by N region addition', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 128] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Translation * Evaluated with [TranslationEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator) | Metric | Value | |:------------------|:-----------| | src2trg_accuracy | 0.0002 | | trg2src_accuracy | 0.0 | | **mean_accuracy** | **0.0001** | ## Training Details ### Training Dataset #### parquet * Dataset: parquet * Size: 40,906 training samples * Columns: anchor and positive * Approximate statistics based on the first 1000 samples: | | anchor | positive | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | anchor | positive | |:------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------| | Catalysis of the transfer of a mannose residue to an oligosaccharide, forming an alpha-(1->6) linkage. | 1,6-alpha-mannosyltransferase activity | | Catalysis of the hydrolysis of ester linkages within a single-stranded deoxyribonucleic acid molecule by creating internal breaks. | single-stranded DNA specific endodeoxyribonuclease activity | | Catalysis of the hydrolysis of ester linkages within a single-stranded deoxyribonucleic acid molecule by creating internal breaks. | ssDNA-specific endodeoxyribonuclease activity | * Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MegaBatchMarginLoss", "matryoshka_dims": [ 64, 32 ], "matryoshka_weights": [ 1, 1 ], "n_dims_per_step": -1 } ``` ### Evaluation Dataset #### parquet * Dataset: parquet * Size: 6,585 evaluation samples * Columns: anchor and positive * Approximate statistics based on the first 1000 samples: | | anchor | positive | |:--------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | anchor | positive | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------| | The maintenance of the structure and integrity of the mitochondrial genome; includes replication and segregation of the mitochondrial chromosome. | mitochondrial genome maintenance | | The repair of single strand breaks in DNA. Repair of such breaks is mediated by the same enzyme systems as are used in base excision repair. | single strand break repair | | Any process that modulates the frequency, rate or extent of DNA recombination, a DNA metabolic process in which a new genotype is formed by reassortment of genes resulting in gene combinations different from those that were present in the parents. | regulation of DNA recombination | * Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MegaBatchMarginLoss", "matryoshka_dims": [ 64, 32 ], "matryoshka_weights": [ 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 10 - `per_device_eval_batch_size`: 5 - `torch_empty_cache_steps`: 200 - `learning_rate`: 0.2 - `weight_decay`: 0.001 - `num_train_epochs`: 1 - `warmup_ratio`: 0.25 - `seed`: 25 - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 10 - `per_device_eval_batch_size`: 5 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: 200 - `learning_rate`: 0.2 - `weight_decay`: 0.001 - `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`: 0.25 - `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`: 25 - `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`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | mean_accuracy | |:-----:|:----:|:-------------:| | 0 | 0 | 0.0001 | ### Framework Versions - Python: 3.12.8 - Sentence Transformers: 3.3.1 - Transformers: 4.47.0 - PyTorch: 2.5.1 - Accelerate: 1.2.0 - Datasets: 3.1.0 - 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", } ``` #### 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} } ``` #### MegaBatchMarginLoss ```bibtex @inproceedings{wieting-gimpel-2018-paranmt, title = "{P}ara{NMT}-50{M}: Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations", author = "Wieting, John and Gimpel, Kevin", editor = "Gurevych, Iryna and Miyao, Yusuke", booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = jul, year = "2018", address = "Melbourne, Australia", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/P18-1042", doi = "10.18653/v1/P18-1042", pages = "451--462", } ```