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---
base_model: mixedbread-ai/mxbai-embed-large-v1
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:7704
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: 'Serve food or beverages.
Provide customers with general information or assistance.
Clean facilities or work areas.'
sentences:
- 'Assess database performance.
Analyze data to identify trends or relationships among variables.
Develop procedures for data management.
Create databases to store electronic data.
Design computer modeling or simulation programs.'
- Feed, water, groom, bathe, exercise, or otherwise provide care to promote and
maintain the well-being of pets and other animals that are not raised for consumption,
such as dogs, cats, race horses, ornamental fish or birds, zoo animals, and mice.
Work in settings such as kennels, animal shelters, zoos, circuses, and aquariums.
May keep records of feedings, treatments, and animals received or discharged.
May clean, disinfect, and repair cages, pens, or fish tanks.
- Dining Room and Cafeteria Attendants and Bartender Helpers - Facilitate food service.
Clean tables; remove dirty dishes; replace soiled table linens; set tables; replenish
supply of clean linens, silverware, glassware, and dishes; supply service bar
with food; and serve items such as water, condiments, and coffee to patrons.
- source_sentence: 'Supervise staff, volunteers, practicum students, or interns.
Teach art therapy techniques or processes to artists, interns, volunteers, or
others.
Photograph or videotape client artwork for inclusion in client records or for
promotional purposes.
Establish goals or objectives for art therapy sessions in consultation with clients
or site administrators.'
sentences:
- 'Financial Managers - Analyze financial records to improve budgeting or planning.
Supervise employees.
Prepare reports related to compliance matters.
Direct organizational operations, projects, or services.
Establish interpersonal business relationships to facilitate work activities.'
- Plan or conduct art therapy sessions or programs to improve clients' physical,
cognitive, or emotional well-being.
- 'Maintain operational records.
Manage organizational or project budgets.
Develop promotional materials.
Analyze risks to minimize losses or damages.'
- source_sentence: 'Tire Repairers and Changers - Disassemble equipment for maintenance
or repair.
Service vehicles to maintain functionality.
Assemble mechanical components or machine parts.'
sentences:
- Select, fit, and take care of costumes for cast members, and aid entertainers.
May assist with multiple costume changes during performances.
- 'Install metal structural components.
Cut metal components for installation.
Position safety or support equipment.
Operate cranes, hoists, or other moving or lifting equipment.
Position structural components.'
- 'Disassemble equipment for maintenance or repair.
Service vehicles to maintain functionality.
Assemble mechanical components or machine parts.'
- source_sentence: 'Orthodontists - Adjust dental devices or appliances to ensure
fit.
Confer with clients to discuss treatment plans or progress.
Design medical devices or appliances.
Train medical providers.
Advise patients on effects of health conditions or treatments.'
sentences:
- 'Maintain surveillance of individuals or establishments.
Apprehend criminal suspects.
Recommend improvements to increase safety or reduce risks.
Administer first aid.
Communicate health and wellness information to the public.
Confiscate prohibited or dangerous items.'
- 'Build models, patterns, or templates.
Operate still or video cameras or related equipment.
Prepare materials for preservation, storage, or display.
Estimate costs for projects or productions.
Coordinate logistics for productions or events.
Perform marketing activities.'
- 'Adjust dental devices or appliances to ensure fit.
Confer with clients to discuss treatment plans or progress.
Design medical devices or appliances.
Train medical providers.
Advise patients on effects of health conditions or treatments.'
- source_sentence: 'Collect samples of materials or products for laboratory testing.
Weigh or measure materials, ingredients, or products to ensure conformance to
requirements.
Stop mixing or blending machines when specified product qualities are obtained
and open valves and start pumps to transfer mixtures.'
sentences:
- 'Load materials into production equipment.
Clear equipment jams.
Test chemical or physical characteristics of materials or products.
Mix substances to create chemical solutions.
Adjust equipment controls to regulate flow of production materials or products.
Clean facilities or work areas.'
- Provide services to ensure the safety of passengers aboard ships, buses, trains,
or within the station or terminal. Perform duties such as explaining the use of
safety equipment, serving meals or beverages, or answering questions related to
travel.
- 'Conduct research to increase knowledge about medical issues.
Evaluate patient functioning, capabilities, or health.
Collaborate with healthcare professionals to plan or provide treatment.
Maintain inventory of medical supplies or equipment.
Process medical billing information.
Clean facilities or equipment.'
---
# SentenceTransformer based on mixedbread-ai/mxbai-embed-large-v1
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1). 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:** [mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) <!-- at revision 526dc52cb738085d87002bf00ca4d3d99fd0029b -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 1024 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **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': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, '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("Daxtra/onet_sbert-v3")
# Run inference
sentences = [
'Collect samples of materials or products for laboratory testing.\nWeigh or measure materials, ingredients, or products to ensure conformance to requirements.\nStop mixing or blending machines when specified product qualities are obtained and open valves and start pumps to transfer mixtures.',
'Load materials into production equipment.\nClear equipment jams.\nTest chemical or physical characteristics of materials or products.\nMix substances to create chemical solutions.\nAdjust equipment controls to regulate flow of production materials or products.\nClean facilities or work areas.',
'Conduct research to increase knowledge about medical issues.\nEvaluate patient functioning, capabilities, or health.\nCollaborate with healthcare professionals to plan or provide treatment.\nMaintain inventory of medical supplies or equipment.\nProcess medical billing information.\nClean facilities or equipment.',
]
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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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 7,704 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 |
|:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 13 tokens</li><li>mean: 70.86 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 41.53 tokens</li><li>max: 115 tokens</li></ul> |
* Samples:
| sentence_0 | sentence_1 |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Textile Knitting and Weaving Machine Setters, Operators, and Tenders - Record operational or production data.<br>Lubricate production equipment.<br>Conduct test runs of production equipment.<br>Clean production equipment.<br>Repair production equipment or tools.</code> | <code>Record operational or production data.<br>Lubricate production equipment.<br>Conduct test runs of production equipment.<br>Clean production equipment.<br>Repair production equipment or tools.</code> |
| <code>Provide technical support for software maintenance or use.<br>Evaluate data quality.<br>Update knowledge about emerging industry or technology trends.<br>Prepare analytical reports.<br>Collaborate with others to resolve information technology issues.<br>Troubleshoot issues with computer applications or systems.</code> | <code>Geographic Information Systems Technologists and Technicians - Assist scientists or related professionals in building, maintaining, modifying, or using geographic information systems (GIS) databases. May also perform some custom application development or provide user support.</code> |
| <code>Prepare pointe shoes, by sewing or other means, for use in rehearsals and performance.<br>Attend costume fittings, photography sessions, and makeup calls associated with dance performances.<br>Perform classical, modern, or acrobatic dances in productions, expressing stories, rhythm, and sound with their bodies.<br>Devise and choreograph dance for self or others.<br>Study and practice dance moves required in roles.<br>Monitor the field of dance to remain aware of current trends and innovations.</code> | <code>Train others on performance techniques.<br>Perform dances.<br>Audition for roles.<br>Choreograph dances.<br>Monitor current trends.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](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`: 24
- `per_device_eval_batch_size`: 24
- `num_train_epochs`: 1
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 24
- `per_device_eval_batch_size`: 24
- `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`: 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`: 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
- `eval_use_gather_object`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
</details>
### Training Logs
| Epoch | Step |
|:------:|:----:|
| 0.0997 | 32 |
| 0.1994 | 64 |
| 0.2991 | 96 |
| 0.3988 | 128 |
| 0.4984 | 160 |
| 0.5981 | 192 |
| 0.6978 | 224 |
| 0.7975 | 256 |
| 0.8972 | 288 |
| 0.9969 | 320 |
| 1.0 | 321 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.2.0
- Transformers: 4.44.2
- PyTorch: 2.4.1+cu121
- Accelerate: 0.34.2
- Datasets: 3.0.1
- 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",
}
```
#### 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}
}
```
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