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---
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:152151
- loss:HardMultipleNegativesRankingLoss
- loss:CachedMultipleNegativesSymmetricRankingLoss
widget:
- source_sentence: Use arc welding techniques to make welds in conditions of very
high pressure, usually in an underwater dry chamber such as a diving bell. Compensate
for the negative consequences of high pressure on a weld, such as the shorter
and less steady welding arc.
sentences:
- skill_skill
- weld in hyperbaric conditions
- human-robot collaboration
- source_sentence: Carry out mineral processing operations, which aim to separate
valuable minerals from waste rock or grout. Oversee and implement processes such
as samping, analysis and most importantly the electrostatic separation process,
which separates valuable materials from mineral ore.
sentences:
- internet governance
- implement mineral processes
- skill_skill
- source_sentence: looking for a pest control technician with strong knowledge in
preventative measures to minimize pest populations A successful candidate will
have experience in cryopreservation techniques as well as laboratory protocols
sentences:
- cryopreservation
- food preservation
- skill_sentence
- source_sentence: Candidates with experience using popular balance sheet software
are encouraged to apply for our accounting position. We are looking for a cargo
handling expert who can maximize efficiency on our shipping vessels.
sentences:
- skill_sentence
- perform balance sheet operations
- promote inclusion
- source_sentence: Must have the ability to read and interpret schematics and effectively
install and calibrate lift governors to ensure compliance with safety standards.
The ideal candidate must have an ear for identifying music with commercial potential
and understand the current market trends.
sentences:
- prepare credit reports
- install lift governor
- skill_sentence
---
# 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 skill_sentence and skill_skill datasets. 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:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) <!-- at revision 9a3225965996d404b775526de6dbfe85d3368642 -->
- **Maximum Sequence Length:** 96 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Datasets:**
- skill_sentence
- skill_skill
<!-- - **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': 96, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): SmartTokenPooling({'word_embedding_dimension': 768, 'window_size': -1})
)
```
## 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/ConTeXT-Skill-Extraction-base")
# Run inference
sentences = [
'Must have the ability to read and interpret schematics and effectively install and calibrate lift governors to ensure compliance with safety standards. The ideal candidate must have an ear for identifying music with commercial potential and understand the current market trends.',
'install lift governor',
'skill_sentence',
]
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]
```
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</details>
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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 Datasets
#### skill_sentence
* Dataset: skill_sentence
* Size: 138,260 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>type</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | type |
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 9 tokens</li><li>mean: 35.67 tokens</li><li>max: 63 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 6.12 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 5.0 tokens</li><li>max: 5 tokens</li></ul> |
* Samples:
| anchor | positive | type |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------|:----------------------------|
| <code>duties for this role will include conducting water chemistry analysis and managing the laboratory. seeking a seasoned print manufacturing manager with knowledge of printing materials, processes and equipment.</code> | <code>water chemistry analysis</code> | <code>skill_sentence</code> |
| <code>divers must understand how to calculate dive times and limits to ensure they return safely. We are searching for a multimedia software expert with experience in sound, lighting and recording software.</code> | <code>comply with the planned time for the depth of the dive</code> | <code>skill_sentence</code> |
| <code>A successful candidate will possess the ability to calibrate laboratory equipment according to industry standards. we are seeking a candidate with experience in preparing government funding dossiers</code> | <code>prepare government funding dossiers</code> | <code>skill_sentence</code> |
* Loss: <code>custom_losses.HardMultipleNegativesRankingLoss</code> with these parameters:
```json
{
"scale": 20,
"similarity_fct": "<lambda>"
}
```
#### skill_skill
* Dataset: skill_skill
* Size: 13,891 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>type</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | type |
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 29.09 tokens</li><li>max: 96 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 6.24 tokens</li><li>max: 16 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 5.0 tokens</li><li>max: 5 tokens</li></ul> |
* Samples:
| anchor | positive | type |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------|:-------------------------|
| <code>Adapt and move set pieces during rehearsals and live performances.</code> | <code>adapt sets</code> | <code>skill_skill</code> |
| <code>Prepare bread and bread products such as sandwiches for consumption.</code> | <code>prepare bread products</code> | <code>skill_skill</code> |
| <code>The strategies, methods and techniques that increase the organisation's capacity to protect and sustain the services and operations that fulfil the organisational mission and create lasting values by effectively addressing the combined issues of security, preparedness, risk and disaster recovery.</code> | <code>organisational resilience</code> | <code>skill_skill</code> |
* Loss: [<code>CachedMultipleNegativesSymmetricRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativessymmetricrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"mini_batch_size": 64
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `overwrite_output_dir`: True
- `eval_strategy`: steps
- `per_device_train_batch_size`: 4096
- `per_device_eval_batch_size`: 4096
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
- `load_best_model_at_end`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: True
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 4096
- `per_device_eval_batch_size`: 4096
- `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`: 0.1
- `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`: 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`: True
- `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 |
|:----------:|:------:|
| 0.1053 | 4 |
| 0.2105 | 8 |
| 0.3158 | 12 |
| 0.4211 | 16 |
| 0.5263 | 20 |
| 0.6316 | 24 |
| **0.7368** | **28** |
| 0.8421 | 32 |
| 0.9474 | 36 |
* The bold row denotes the saved checkpoint.
### 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",
}
```
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