Metadata-Version: 2.1
Name: esco-skill-extractor
Version: 0.1.8
Summary: Extract ESCO skills from texts such as job descriptions or CVs
Home-page: https://github.com/KonstantinosPetrakis/esco-skill-extractor
Author: Konstantinos Petrakis
Author-email: konstpetrakis01@gmail.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
Provides-Extra: cuda
License-File: LICENSE

# ESCO Skill Extractor

This is a a tool that extract **ESCO skills from texts** such as job descriptions or CVs. It uses a transformer and compares its embedding using cosine similarity. 

## Installation

```bash
pip install esco-skill-extractor
```

or for Nvidia GPU acceleration:

```bash
pip install esco-skill-extractor[cuda]
```


## Usage

```python
from esco_skill_extractor import SkillExtractor

# Don't be scared, the 1st time will take a longer to download the models and create the embeddings.
# `device` kwarg is optional and defaults to 'cpu', `cuda` or others can be used.
# `threshold` kwarg is optional and defaults to 0.4, it's the cosine similarity threshold.
skill_extractor = SkillExtractor()

ads = [
    "We are looking for a software engineer with experience in Java and Python.",
    "We are looking for a devops engineer. Containerization tools such as Docker is a must. AWS is a plus."
    # ...
]

print(skill_extractor.get_skills(ads))

# Output:
# [
#     [
#         "http://data.europa.eu/esco/skill/ccd0a1d9-afda-43d9-b901-96344886e14d"
#     ],
#     [
#         "http://data.europa.eu/esco/skill/f0de4973-0a70-4644-8fd4-3a97080476f4",
#         "http://data.europa.eu/esco/skill/ae4f0cc6-e0b9-47f5-bdca-2fc2e6316dce",
#     ],
# ]
# ]
```

## How it works

1. It creates embeddings from esco skills found in the official ESCO website.
2. It creates embeddings from the input text (one for each sentence).
3. It compares the embeddings of the text with the embeddings of the ESCO skills using cosine similarity.
4. It returns the most similar esco skill per sentence if its similarity passes a predefined threshold.


