semvec/pypi-packages huggingface dataset:
sparv-sbx-word-prediction-kb-bert 0.6.1
A sparv plugin for computing word neighbours using a BERT model.
License: MIT
Requires Python: >=3.9

# sparv-sbx-word-prediction-kb-bert

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Plugin for applying bert masking as a [Sparv](https://github.com/spraakbanken/sparv-pipeline) annotation.

## Install

First, install Sparv, as suggested:

```bash
pipx install sparv-pipeline
```

Then install install `sparv-sbx-word-prediction-kb-bert` with

```bash
pipx inject sparv-pipeline sparv-sbx-word-prediction-kb-bert
```

## Usage

Depending on how many explicit exports of annotations you have you can decide to use this
annotation exclusively by adding it as the only annotation to export under `xml_export`:

```yaml
xml_export:
    annotations:
        - <token>:sbx_word_prediction_kb_bert.word-prediction--kb-bert
```

To use it together with other annotations you might add it under `export`:

```yaml
export:
    annotations:
        - <token>:sbx_word_prediction_kb_bert.word-prediction--kb-bert
        ...
```

### Configuration

You can configure this plugin by the number of neighbours to generate.

#### Number of Neighbours

The number of neighbours defaults to `5` but can be configured in `config.yaml`:

```yaml
sbx_word_prediction_kb_bert:
    num_neighbours: 5
```

#### Number of Decimals

The number of decimals defaults to `3` but can be configured in `config.yaml`:

```yaml
sbx_word_prediction_kb_bert:
    num_decimals: 3
```

> [!NOTE] This also controls the cut-off, so all values where the score round to 0.000 (or the number of decimals) is discarded.

### Metadata

#### Model

Type | HuggingFace Model | Revision
--- | --- | ---
Model | [`KBLab/bert-base-swedish-cased`](https://huggingface.co/KBLab/bert-base-swedish-cased) | c710fb8dff81abb11d704cd46a8a1e010b2b022c
Tokenizer | same as Model  | same as Model

## Changelog

This project keeps a [changelog](./CHANGELOG.md).
getinstance 0.7
getinstance
License: MIT
Requires Python: >=3.11


## Installation

```
pip install getinstance
```

## Usage

```python
from getinstance import InstanceManager

class Country:
    instances = InstanceManager()
    
    def __init__(self, name):
        self.name = name
        
    def hello(self, username):
        print(f'hello, {username} from {self.name}')
            
au = Country('Australia')
ru = Country('Russia')

print(list(Country.instances.all()))
print(Country.instances.get(name='Australia')) 
Country.instances.filter(name='Russia').hello(username='Alice')

```