README.md

---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity

---

# dwulff/mpnet-personality

This is a [sentence-transformers](https://www.SBERT.net) model that maps personality-related items or texts into a 768-dimensional dense vector space and can be used for many tasks in personality psychology.

The model has been generated by fine-tuning [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) using unsigned empirical correlations of 200k pairs of personality items. The model, therefore, encodes the content of personality-related texts independent of the direction (e.g., negation). 

See [Wulff & Mata (2024)](https://osf.io/preprints/psyarxiv/9h7aw) (see [Supplement](https://osf.io/nmv29/)) for details.

## Usage

Make sure [sentence-transformers](https://www.SBERT.net) is installed:

```
# latest version
pip install -U sentence-transformers

# latest dev version
pip install git+https://github.com/UKPLab/sentence-transformers.git
```

You can extract embeddings in the following way:

```python
from sentence_transformers import SentenceTransformer

# personality sentences
sentences = ["Rarely think about how I feel.", "Make decisions quickly."]

# load model
model = SentenceTransformer('dwulff/mpnet-personality')

# extract embeddings
embeddings = model.encode(sentences)
print(embeddings)
```

## Evaluation Results

The model has been evaluated on public personality data. For standard personality inventories, such as the BIG5 or HEXACO inventories, the model predicts the empirical correlations between personality items at Pearson r ~ .6 and empirical correlations between scales at Pearson r ~ .7. 

Performance can be higher on the many common personality items it has been trained on due to memorization (r ~ .9). Performance will be worse for more specialized personality assessments and texts beyond personality items, as well as for personality factors due to the reduced variance in correlations.

See [Wulff & Mata (2024)](https://osf.io/preprints/psyarxiv/9h7aw) (see [Supplement](https://osf.io/z47qs/)) for details. 

## Citing

```
@article{wulff2024jinglejangle,
 author = {Wulff, Dirk U. and Mata, Rui},
 title = {Automated jingle–jangle detection: Using embeddings to tackle taxonomic incommensurability},
 journal = {PsyArViv},
 doi = {https://doi.org/10.31234/osf.io/9h7aw}
}

```

## Training
The model was trained with the parameters:

**DataLoader**:

`torch.utils.data.dataloader.DataLoader` of length 3125 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```

**Loss**:

`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` 

Parameters of the fit()-Method:
```
{
 "epochs": 3,
 "evaluation_steps": 0,
 "evaluator": "NoneType",
 "max_grad_norm": 1,
 "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
 "optimizer_params": {
 "lr": 2e-05
 },
 "scheduler": "WarmupLinear",
 "steps_per_epoch": null,
 "warmup_steps": 625,
 "weight_decay": 0.01
}
```


## Full Model Architecture
```
SentenceTransformer(
 (0): Transformer({'max_seq_length': 384, '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): Normalize()
)
```