--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:10053 - loss:MultipleNegativesRankingLoss base_model: allenai/specter2_base widget: - source_sentence: Fluorescence quenching of tryptophan residues sentences: - 'Fluorescence of buried tyrosine residues in proteins. ' - 'A fluorescence quenching study of tryptophanyl residues of (Ca2+ + Mg2+)-ATPase from sarcoplasmic reticulum. ' - 'Some hormonal influences on the acetylation of sulfanilamide in vivo. ' - source_sentence: Human migration to the Americas sentences: - 'Homo sapiens in the Americas. Overview of the earliest human expansion in the New World. ' - 'Profiles of College Drinkers Defined by Alcohol Behaviors at the Week Level: Replication Across Semesters and Prospective Associations With Hazardous Drinking and Dependence-Related Symptoms. ' - 'Human migration. ' - source_sentence: Human Mobility Prediction sentences: - 'Human mobility prediction from region functions with taxi trajectories. ' - 'Understanding Human Mobility from Twitter. ' - 'Ovarian cancer gene therapy using HPV-16 pseudovirion carrying the HSV-tk gene. ' - source_sentence: Nevirapine Resistance sentences: - 'Nevirapine toxicity. ' - 'Recognizing rhenium. ' - 'Update on nevirapine: quest for a niche. ' - source_sentence: EHL tendon reconstruction sentences: - 'A Combined Surgical Approach for Extensor Hallucis Longus Reconstruction: Two Case Reports. ' - 'Flexor tendon reconstruction. ' - 'Noble gases and neuroprotection: summary of current evidence. ' pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy - dot_accuracy - manhattan_accuracy - euclidean_accuracy - max_accuracy model-index: - name: SentenceTransformer based on allenai/specter2_base results: - task: type: triplet name: Triplet dataset: name: triplet dev type: triplet-dev metrics: - type: cosine_accuracy value: 0.573 name: Cosine Accuracy - type: dot_accuracy value: 0.455 name: Dot Accuracy - type: manhattan_accuracy value: 0.576 name: Manhattan Accuracy - type: euclidean_accuracy value: 0.577 name: Euclidean Accuracy - type: max_accuracy value: 0.577 name: Max Accuracy --- # SentenceTransformer based on allenai/specter2_base This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [allenai/specter2_base](https://huggingface.co/allenai/specter2_base) on the json 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:** [allenai/specter2_base](https://huggingface.co/allenai/specter2_base) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 tokens - **Similarity Function:** Cosine Similarity - **Training Dataset:** - json ### 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: PeftModelForFeatureExtraction (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}) ) ``` ## 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 = [ 'EHL tendon reconstruction', 'A Combined Surgical Approach for Extensor Hallucis Longus Reconstruction: Two Case Reports. ', 'Flexor tendon reconstruction. ', ] 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 #### Triplet * Dataset: `triplet-dev` * Evaluated with [TripletEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) | Metric | Value | |:--------------------|:----------| | **cosine_accuracy** | **0.573** | | dot_accuracy | 0.455 | | manhattan_accuracy | 0.576 | | euclidean_accuracy | 0.577 | | max_accuracy | 0.577 | ## Training Details ### Training Dataset #### json * Dataset: json * Size: 10,053 training samples * Columns: anchor, positive, and negative * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | anchor | positive | negative | |:-------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------| | COM-induced secretome changes in U937 monocytes | Characterization of calcium oxalate crystal-induced changes in the secretome of U937 human monocytes. | Monocytes. | | Metamaterials | Sound attenuation optimization using metaporous materials tuned on exceptional points. | Metamaterials: A cat's eye for all directions. | | Pediatric Parasitology | Parasitic infections among school age children 6 to 11-years-of-age in the Eastern province. | [DIALOGUE ON PEDIATRIC PARASITOLOGY]. | * 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`: 512 - `per_device_eval_batch_size`: 512 - `learning_rate`: 0.001 - `num_train_epochs`: 1 - `lr_scheduler_type`: cosine_with_restarts - `warmup_ratio`: 0.1 - `bf16`: 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`: 512 - `per_device_eval_batch_size`: 512 - `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`: 0.001 - `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`: cosine_with_restarts - `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`: 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 - `use_liger_kernel`: False - `eval_use_gather_object`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | triplet-dev_cosine_accuracy | |:-----:|:----:|:-------------:|:---------------------------:| | 0 | 0 | - | 0.373 | | 0.05 | 1 | 4.5633 | - | | 0.1 | 2 | 4.5857 | - | | 0.15 | 3 | 4.1852 | - | | 0.2 | 4 | 3.2547 | - | | 0.25 | 5 | 2.3117 | - | | 0.3 | 6 | 1.949 | - | | 0.35 | 7 | 1.7767 | - | | 0.4 | 8 | 1.79 | - | | 0.45 | 9 | 1.6081 | - | | 0.5 | 10 | 1.7499 | - | | 0.55 | 11 | 1.6395 | - | | 0.6 | 12 | 1.5645 | - | | 0.65 | 13 | 1.5804 | - | | 0.7 | 14 | 1.5303 | - | | 0.75 | 15 | 1.5452 | - | | 0.8 | 16 | 1.5012 | - | | 0.85 | 17 | 1.5283 | - | | 0.9 | 18 | 1.5982 | - | | 0.95 | 19 | 1.4714 | - | | 1.0 | 20 | 1.3331 | 0.573 | ### Framework Versions - Python: 3.9.19 - Sentence Transformers: 3.1.1 - Transformers: 4.45.2 - PyTorch: 2.5.0 - Accelerate: 1.0.1 - Datasets: 2.19.0 - Tokenizers: 0.20.3 ## 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} } ```