semvec/pypi-packages huggingface dataset:
entity-linking 0.0.1.0
A library for lightweight entity linking
Requires Python: >=3.10

Lightweight entity linking solution.

**Please consider citing our works if you use code from this repository.**

## Main dependencies
* python>=3.10
* numpy==1.26.4
* SPARQLWrapper==2.0.0
* sentence_transformers==3.1.1
* aiohttp==3.9.5
* openai==1.54.2
* beautifulsoup4==4.12.2
* fake-useragent==1.5.1

## Example & Usage

```
from linking import main

# Your API token which can be found here (https://github.com/marketplace/models/azure-openai/gpt-4o)
api_token = "YOUR_API_TOKEN"

main.EL(api_token=api_token, sentence="We used PCA and FA for our experiments.", mention="PCA", single="No", combination="No", disambiguation="No", embedding_model="Lajavaness/bilingual-embedding-large")
```

```
Wikipedia: https://en.wikipedia.org/wiki/Principal_component_analysis

Wikidata: https://www.wikidata.org/wiki/Q2873

DBpedia: http://dbpedia.org/resource/Principal_component_analysis

Execution Time: 00:00:18
```

## Parameters
api_token: Your API token from [here] (https://github.com/marketplace/models/azure-openai/gpt-4o). **(Required)**
sentence: An english text. **(Required)**
mention: The mention you want to perform the linking, the mention should be from inside the provided sentence. **(Required)**
single: Usually used for difficult mentions, it searches each word of the mention individually, *(deafult="No")*, *(Values: "Yes", "No")*. *(Optional)*
combination: Usually used for difficult mentions, it makes combinations for each word of the mention, *(deafult="No")*, *(Values: "Yes", "No")*. *(Optional)*
embedding_model: A [sentence-transformers] (https://sbert.net/) model to perform text similarity, *(deafault="Lajavaness/bilingual-embedding-large")*, *(Values: str of the name of any sentence-transformers model)*. *(Optional)*

## Licence
This library is licensed under the CC-BY-NC 4.0 license.