sentence-transformers safetensors mpnet sentence-similarity feature-extraction generated_from_trainer dataset_size:5579240 loss:CachedMultipleNegativesRankingLoss arxiv:1908.10084 arxiv:2101.06983 base_model:sentence-transformers/all-mpnet-base-v2 base_model:finetune:sentence-transformers/all-mpnet-base-v2 autotrain_compatible endpoints_compatible region:us
---
base_model: sentence-transformers/all-mpnet-base-v2
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:5579240
- loss:CachedMultipleNegativesRankingLoss
widget:
- source_sentence: Program Coordinator RN
  sentences:
  - discuss the medical history of the healthcare user, evidence-based approach in
    general practice, apply various lifting techniques, establish daily priorities,
    manage time, demonstrate disciplinary expertise, tolerate sitting for long periods,
    think critically, provide professional care in nursing, attend meetings, represent
    union members, nursing science, manage a multidisciplinary team involved in patient
    care, implement nursing care, customer service, work under supervision in care,
    keep up-to-date with training subjects, evidence-based nursing care, operate lifting
    equipment, follow code of ethics for biomedical practices, coordinate care, provide
    learning support in healthcare
  - provide written content, prepare visual data, design computer network, deliver
    visual presentation of data, communication, operate relational database management
    system, ICT communications protocols, document management, use threading techniques,
    search engines, computer science, analyse network bandwidth requirements, analyse
    network configuration and performance, develop architectural plans, conduct ICT
    code review, hardware architectures, computer engineering, video-games functionalities,
    conduct web searches, use databases, use online tools to collaborate
  - nursing science, administer appointments, administrative tasks in a medical environment,
    intravenous infusion, plan nursing care, prepare intravenous packs, work with
    nursing staff, supervise nursing staff, clinical perfusion
- source_sentence: Director of Federal Business Development and Capture Mgmt
  sentences:
  - develop business plans, strive for company growth, develop personal skills, channel
    marketing, prepare financial projections, perform market research, identify new
    business opportunities, market research, maintain relationship with customers,
    manage government funding, achieve sales targets, build business relationships,
    expand the network of providers, make decisions, guarantee customer satisfaction,
    collaborate in the development of marketing strategies, analyse business plans,
    think analytically, develop revenue generation strategies, health care legislation,
    align efforts towards business development, assume responsibility, solve problems,
    deliver business research proposals, identify potential markets for companies
  - operate warehouse materials, goods transported from warehouse facilities, organise
    social work packages, coordinate orders from various suppliers, warehouse operations,
    work in assembly line teams, work in a logistics team, footwear materials
  - manufacturing plant equipment, use hand tools, assemble hardware components, use
    traditional toolbox tools, perform product testing, control panel components,
    perform pre-assembly quality checks, oversee equipment operation, assemble mechatronic
    units, arrange equipment repairs, assemble machines, build machines, resolve equipment
    malfunctions, electromechanics, develop assembly instructions, install hydraulic
    systems, revise quality control systems documentation, detect product defects,
    operate hydraulic machinery controls, show an exemplary leading role in an organisation,
    assemble manufactured pipeline parts, types of pallets, perform office routine
    activities, conform with production requirements, comply with quality standards
    related to healthcare practice
- source_sentence: director of production
  sentences:
  - use customer relationship management software, sales strategies, create project
    specifications, document project progress, attend trade fairs, building automation,
    sales department processes, work independently, develop account strategy, build
    business relationships, facilitate the bidding process, close sales at auction,
    satisfy technical requirements, results-based management, achieve sales targets,
    manage sales teams, liaise with specialist contractors for well operations, sales
    activities, use sales forecasting softwares, guarantee customer satisfaction,
    integrate building requirements in the architectural design, participate actively
    in civic life, customer relationship management, implement sales strategies
  - translate strategy into operation, lead the brand strategic planning process,
    assist in developing marketing campaigns, implement sales strategies, sales promotion
    techniques, negotiate with employment agencies, perform market research, communicate
    with customers, develop media strategy, change power distribution systems, beverage
    products, project management, provide advertisement samples, devise military tactics,
    use microsoft office, market analysis, manage sales teams, create brand guidelines,
    brand marketing techniques, use sales forecasting softwares, supervise brand management,
    analyse packaging requirements, provide written content, hand out product samples,
    channel marketing
  - use microsoft office, use scripting programming, build team spirit, operate games,
    production processes, create project specifications, analyse production processes
    for improvement, manage production enterprise, Agile development, apply basic
    programming skills, document project progress, supervise game operations, work
    to develop physical ability to perform at the highest level in sport, fix meetings,
    office software, enhance production workflow, manage a team, set production KPI,
    manage commercial risks, work in teams, teamwork principles, address identified
    risks, meet deadlines, consult with production director
- source_sentence: Nursing Assistant
  sentences:
  - supervise medical residents, observe healthcare users, provide domestic care,
    prepare health documentation, position patients undergoing interventions, work
    with broad variety of personalities, supervise food in healthcare, tend to elderly
    people, monitor patient's vital signs, transfer patients, show empathy, provide
    in-home support for disabled individuals, hygiene in a health care setting, supervise
    housekeeping operations, perform cleaning duties, monitor patient's health condition,
    provide basic support to patients, work with nursing staff, involve service users
    and carers in care planning, use electronic health records management system,
    arrange in-home services for patients, provide nursing care in community settings
    , work in shifts, supervise nursing staff
  - manage relationships with stakeholders, use microsoft office, maintain records
    of financial transactions, software components suppliers, tools for software configuration
    management, attend to detail, keep track of expenses, build business relationships,
    issue sales invoices, financial department processes, supplier management, process
    payments, perform records management, manage standard enterprise resource planning
    system
  - inspect quality of products, apply HACCP, test package, follow verbal instructions,
    laboratory equipment, assist in the production of laboratory documentation, ensure
    quality control in packaging, develop food safety programmes, packaging engineering,
    appropriate packaging of dangerous goods, maintain laboratory equipment, SAP Data
    Services, calibrate laboratory equipment, analyse packaging requirements, write
    English
- source_sentence: Branch Manager
  sentences:
  - support employability of people with disabilities, schedule shifts, issue licences,
    funding methods, maintain correspondence records, computer equipment, decide on
    providing funds, tend filing machine, use microsoft office, lift stacks of paper,
    transport office equipment, tend to guests with special needs, provide written
    content, foreign affairs policy development, provide charity services, philanthropy,
    maintain financial records, meet deadlines, manage fundraising activities, assist
    individuals with disabilities in community activities, report on grants, prepare
    compliance documents, manage grant applications, tolerate sitting for long periods,
    follow work schedule
  - cook pastry products, create new recipes, food service operations, assess shelf
    life of food products, apply requirements concerning manufacturing of food and
    beverages, food waste monitoring systems, maintain work area cleanliness, comply
    with food safety and hygiene, coordinate catering, maintain store cleanliness,
    work according to recipe, health, safety and hygiene legislation, install refrigeration
    equipment, prepare desserts, measure precise food processing operations, conform
    with production requirements, work in an organised manner, demand excellence from
    performers, refrigerants, attend to detail, ensure food quality, manufacture prepared
    meals
  - teamwork principles, office administration, delegate responsibilities, create
    banking accounts, manage alarm system, make independent operating decisions, use
    microsoft office, offer financial services, ensure proper document management,
    own management skills, use spreadsheets software, manage cash flow, integrate
    community outreach, manage time, perform multiple tasks at the same time, carry
    out calculations, assess customer credibility, maintain customer service, team
    building, digitise documents, promote financial products, communication, assist
    customers, follow procedures in the event of an alarm, office equipment
---

# 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 generator dataset. It maps sentences & paragraphs to a 1024-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) <!-- at revision 9a3225965996d404b775526de6dbfe85d3368642 -->
- **Maximum Sequence Length:** 64 tokens
- **Output Dimensionality:** 1024 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - generator
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### 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': 64, '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): Asym(
    (anchor-0): Dense({'in_features': 768, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
    (positive-0): Dense({'in_features': 768, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
  )
)
```

## 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("jensjorisdecorte/JobBERT-v2")
# Run inference
sentences = [
    'Branch Manager',
    'teamwork principles, office administration, delegate responsibilities, create banking accounts, manage alarm system, make independent operating decisions, use microsoft office, offer financial services, ensure proper document management, own management skills, use spreadsheets software, manage cash flow, integrate community outreach, manage time, perform multiple tasks at the same time, carry out calculations, assess customer credibility, maintain customer service, team building, digitise documents, promote financial products, communication, assist customers, follow procedures in the event of an alarm, office equipment',
    'support employability of people with disabilities, schedule shifts, issue licences, funding methods, maintain correspondence records, computer equipment, decide on providing funds, tend filing machine, use microsoft office, lift stacks of paper, transport office equipment, tend to guests with special needs, provide written content, foreign affairs policy development, provide charity services, philanthropy, maintain financial records, meet deadlines, manage fundraising activities, assist individuals with disabilities in community activities, report on grants, prepare compliance documents, manage grant applications, tolerate sitting for long periods, follow work schedule',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

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## Training Details

### Training Dataset

#### generator

* Dataset: generator
* Size: 5,579,240 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                           | positive                                                                           |
  |:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                             |
  | details | <ul><li>min: 3 tokens</li><li>mean: 7.95 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 59.33 tokens</li><li>max: 64 tokens</li></ul> |
* Samples:
  | anchor                                                              | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         |
  |:--------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>CAD Designer - Fire Sprinkler - Milwaukee - Relocation</code> | <code>coordinate construction activities, oversee construction project, fire protection engineering, install fire sprinklers, hydraulics, construction industry, create AutoCAD drawings, design sprinkler systems, inspect construction sites, design drawings, supervise sewerage systems construction, prepare site for construction, building codes, communicate with construction crews</code>                                                                                                                                                                                                                              |
  | <code>RN Practitioner</code>                                        | <code>assume responsibility, financial statements, manage work, implement fundamentals of nursing, diagnose advanced nursing care, diagnose nursing care, specialist nursing care, nursing principles, provide nursing advice on healthcare, apply nursing care in long-term care, prescribe advanced nursing care, plan advanced nursing care, nursing science, implement nursing care, develop financial statistics reports, clinical decision-making at advanced practice, prepare financial statements, create a financial report, produce statistical financial records, operate in a specific field of nursing care</code> |
  | <code>Respiratory Therapist Travel Positions (BB-160B7)</code>      | <code>respiratory therapy, comply with quality standards related to healthcare practice, provide information, primary care, record treated patient's information, formulate a treatment plan, carry out treatment prescribed by doctors, develop patient treatment strategies</code>                                                                                                                                                                                                                                                                                                                                             |
* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `overwrite_output_dir`: True
- `per_device_train_batch_size`: 2048
- `per_device_eval_batch_size`: 2048
- `num_train_epochs`: 1
- `fp16`: True

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: True
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 2048
- `per_device_eval_batch_size`: 2048
- `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`: 1.0
- `num_train_epochs`: 1
- `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`: 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`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.1835 | 500  | 3.6354        |
| 0.3670 | 1000 | 3.1788        |
| 0.5505 | 1500 | 2.9969        |
| 0.7339 | 2000 | 2.9026        |
| 0.9174 | 2500 | 2.8421        |


### Framework Versions
- Python: 3.9.19
- Sentence Transformers: 3.1.0
- Transformers: 4.44.2
- PyTorch: 2.4.1+cu118
- Accelerate: 0.34.2
- Datasets: 3.0.0
- 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",
}
```

#### CachedMultipleNegativesRankingLoss
```bibtex
@misc{gao2021scaling,
    title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
    author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
    year={2021},
    eprint={2101.06983},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}
```

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