--- tags: - sentence-transformers - sentence-similarity - feature-extraction - dense - generated_from_trainer - dataset_size:19992 - loss:MultipleNegativesRankingLoss widget: - source_sentence: 'Role: Full Stack Engineer Skills: JavaScript, APIs, React, Databases, Node.js' sentences: - 'Job Title: Product Manager Skills: Roadmap, Stakeholders, PRD, Agile, Strategy' - 'Job Title: Full Stack Engineer Skills: JavaScript, APIs, React, Databases, Node.js' - 'Job Title: Full Stack Engineer Skills: JavaScript, APIs, React, Databases, Node.js' - source_sentence: 'Role: Mechanical Engineer Skills: CAD, Thermodynamics, Manufacturing' sentences: - 'Job Title: Frontend Engineer Skills: JavaScript, React, CSS, HTML, UI' - 'Job Title: Operations Manager Skills: KPIs, Process Improvement, Planning, Stakeholders' - 'Job Title: Mechanical Engineer Skills: CAD, Thermodynamics, Manufacturing' - source_sentence: 'Role: Backend Engineer Skills: Python, APIs, Databases, Docker, OOP' sentences: - 'Job Title: Full Stack Engineer Skills: JavaScript, APIs, React, Databases, Node.js' - 'Job Title: Backend Engineer Skills: Python, APIs, Databases, Docker, OOP' - 'Job Title: Backend Engineer Skills: Python, APIs, Databases, Docker, OOP' - source_sentence: 'Role: Civil Engineer Skills: AutoCAD, Structural Analysis, Construction, Planning' sentences: - 'Job Title: Mechanical Engineer Skills: CAD, Thermodynamics, Manufacturing' - 'Job Title: Mechanical Engineer Skills: CAD, Thermodynamics, Manufacturing' - 'Job Title: Civil Engineer Skills: AutoCAD, Structural Analysis, Construction, Planning' - source_sentence: 'Role: Data Scientist Skills: Python, Statistics, Pandas, ML, Visualization, Modeling' sentences: - 'Job Title: Mechanical Engineer Skills: CAD, Thermodynamics, Manufacturing' - 'Job Title: Data Scientist Skills: Python, Statistics, Pandas, ML, Visualization, Modeling' - 'Job Title: Civil Engineer Skills: AutoCAD, Structural Analysis, Construction, Planning' pipeline_tag: sentence-similarity library_name: sentence-transformers --- # SentenceTransformer This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 384-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:** 256 tokens - **Output Dimensionality:** 384 dimensions - **Similarity Function:** Cosine Similarity ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/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': 256, 'do_lower_case': False, 'architecture': 'BertModel'}) (1): Pooling({'word_embedding_dimension': 384, '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 = [ 'Role: Data Scientist\nSkills: Python, Statistics, Pandas, ML, Visualization, Modeling', 'Job Title: Data Scientist\nSkills: Python, Statistics, Pandas, ML, Visualization, Modeling', 'Job Title: Civil Engineer\nSkills: AutoCAD, Structural Analysis, Construction, Planning', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 384] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities) # tensor([[1.0000, 0.9988, 0.2972], # [0.9988, 1.0000, 0.3066], # [0.2972, 0.3066, 1.0000]]) ``` ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 19,992 training samples * Columns: sentence_0 and sentence_1 * Approximate statistics based on the first 1000 samples: | | sentence_0 | sentence_1 | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence_0 | sentence_1 | |:---------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------| | Role: Operations Manager
Skills: KPIs, Process Improvement, Planning, Stakeholders | Job Title: Operations Manager
Skills: KPIs, Process Improvement, Planning, Stakeholders | | Role: Frontend Engineer
Skills: JavaScript, React, CSS, HTML, UI | Job Title: Frontend Engineer
Skills: JavaScript, React, CSS, HTML, UI | | Role: Full Stack Engineer
Skills: JavaScript, APIs, React, Databases, Node.js | Job Title: Full Stack Engineer
Skills: JavaScript, APIs, React, Databases, Node.js | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim", "gather_across_devices": false, "directions": [ "query_to_doc" ], "partition_mode": "joint", "hardness_mode": null, "hardness_strength": 0.0 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 96 - `per_device_eval_batch_size`: 96 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters
Click to expand - `per_device_train_batch_size`: 96 - `num_train_epochs`: 3 - `max_steps`: -1 - `learning_rate`: 5e-05 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: None - `warmup_steps`: 0 - `optim`: adamw_torch_fused - `optim_args`: None - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `optim_target_modules`: None - `gradient_accumulation_steps`: 1 - `average_tokens_across_devices`: True - `max_grad_norm`: 1 - `label_smoothing_factor`: 0.0 - `bf16`: False - `fp16`: False - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `use_liger_kernel`: False - `liger_kernel_config`: None - `use_cache`: False - `neftune_noise_alpha`: None - `torch_empty_cache_steps`: None - `auto_find_batch_size`: False - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `include_num_input_tokens_seen`: no - `log_level`: passive - `log_level_replica`: warning - `disable_tqdm`: False - `project`: huggingface - `trackio_space_id`: trackio - `eval_strategy`: no - `per_device_eval_batch_size`: 96 - `prediction_loss_only`: True - `eval_on_start`: False - `eval_do_concat_batches`: True - `eval_use_gather_object`: False - `eval_accumulation_steps`: None - `include_for_metrics`: [] - `batch_eval_metrics`: False - `save_only_model`: False - `save_on_each_node`: False - `enable_jit_checkpoint`: False - `push_to_hub`: False - `hub_private_repo`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_always_push`: False - `hub_revision`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `restore_callback_states_from_checkpoint`: False - `full_determinism`: False - `seed`: 42 - `data_seed`: None - `use_cpu`: False - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `parallelism_config`: None - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `dataloader_prefetch_factor`: None - `remove_unused_columns`: True - `label_names`: None - `train_sampling_strategy`: random - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `ddp_backend`: None - `ddp_timeout`: 1800 - `fsdp`: [] - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `deepspeed`: None - `debug`: [] - `skip_memory_metrics`: True - `do_predict`: False - `resume_from_checkpoint`: None - `warmup_ratio`: None - `local_rank`: -1 - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin - `router_mapping`: {} - `learning_rate_mapping`: {}
### Training Logs | Epoch | Step | Training Loss | |:------:|:----:|:-------------:| | 2.3923 | 500 | 2.2303 | ### Framework Versions - Python: 3.12.7 - Sentence Transformers: 5.3.0 - Transformers: 5.3.0 - PyTorch: 2.10.0+cu130 - Accelerate: 1.13.0 - Datasets: 4.7.0 - Tokenizers: 0.22.2 ## 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{oord2019representationlearningcontrastivepredictive, title={Representation Learning with Contrastive Predictive Coding}, author={Aaron van den Oord and Yazhe Li and Oriol Vinyals}, year={2019}, eprint={1807.03748}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/1807.03748}, } ```