esco-skill-extractor 0.1.18

Extract ESCO skills from texts such as job descriptions or CVs

ESCO Skill Extractor

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

Installation

pip install esco-skill-extractor

Installation via Docker

docker run -it -p 5000:5000 konstantinospetrakis/esco-skill-extractor

Usage

Via python

from esco_skill_extractor import SkillExtractor

# Don't be scared, the 1st time will take longer to download the model and create the embeddings.
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))
# [
#     [
#         "http://data.europa.eu/esco/skill/19a8293b-8e95-4de3-983f-77484079c389",
#         "http://data.europa.eu/esco/skill/ccd0a1d9-afda-43d9-b901-96344886e14d",
#     ],
#     [
#         "http://data.europa.eu/esco/skill/11430d93-c835-48ed-8e70-285fa69c9ae6",
#         "http://data.europa.eu/esco/skill/ae4f0cc6-e0b9-47f5-bdca-2fc2e6316dce",
#         "http://data.europa.eu/esco/skill/ce8ae6ca-61d8-4174-b457-641de96cbff4",
#         "http://data.europa.eu/esco/skill/f0de4973-0a70-4644-8fd4-3a97080476f4",
#     ],
# ]
print(skill_extractor.get_occupations(ads))
# [
#     [
#         "http://data.europa.eu/esco/occupation/10469d70-78a3-4650-9e29-d04de13c62c1",
#         "http://data.europa.eu/esco/occupation/1c5a896a-e010-4217-a29a-c44db26e25da",
#         "http://data.europa.eu/esco/occupation/4874fa37-0cd1-4a68-aed8-a838851f242d",
#         "http://data.europa.eu/esco/occupation/579254cf-6d69-4889-9000-9c79dc568644",
#         "http://data.europa.eu/esco/occupation/57af9090-55b4-4911-b2d0-86db01c00b02",
#         "http://data.europa.eu/esco/occupation/f2b15a0e-e65a-438a-affb-29b9d50b77d1",
#         "http://data.europa.eu/esco/isco/C2512",
#         "http://data.europa.eu/esco/isco/C2514",
#     ],
#     [
#         "http://data.europa.eu/esco/occupation/2fb96c6c-8d0b-4ef0-b1ee-3e493305e4eb",
#         "http://data.europa.eu/esco/occupation/349ee6f6-c295-4c38-9b98-48765b55280e",
#         "http://data.europa.eu/esco/occupation/781a6350-e686-45b9-b075-e4c8d5a05ff7",
#         "http://data.europa.eu/esco/occupation/93b11f0f-69af-4ece-b9da-f29aab7d38d3",
#         "http://data.europa.eu/esco/occupation/bb609566-3ab6-44dd-8f48-cf0b15b96827",
#         "http://data.europa.eu/esco/occupation/cc867bee-ab5c-427f-9244-f7a204d9574b",
#     ],
# ]

Via GUI

# Visit the URL printed in the console.
# run python -m esco_skill_extractor --help for more options.
python -m esco_skill_extractor

Via API

# Visit the URL printed in the console.
# run python -m esco_skill_extractor --help for more options.
python -m esco_skill_extractor

async function getSkills() {
  const texts = [
    "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.",
    // ...
  ];

  // Default host is localhost, and default port is 8000. Check CLI options for more.
  const response = await fetch("http://localhost:8000/extract-skills", {
    method: "POST",
    headers: {
      "Content-Type": "application/json",
    },
    body: JSON.stringify(texts),
  });

  const skills = await response.json();
  console.log(skills);
  //  [
  //    [
  //      "http://data.europa.eu/esco/skill/19a8293b-8e95-4de3-983f-77484079c389",
  //      "http://data.europa.eu/esco/skill/ccd0a1d9-afda-43d9-b901-96344886e14d",
  //    ],
  //    [
  //      "http://data.europa.eu/esco/skill/11430d93-c835-48ed-8e70-285fa69c9ae6",
  //      "http://data.europa.eu/esco/skill/ae4f0cc6-e0b9-47f5-bdca-2fc2e6316dce",
  //      "http://data.europa.eu/esco/skill/ce8ae6ca-61d8-4174-b457-641de96cbff4",
  //      "http://data.europa.eu/esco/skill/f0de4973-0a70-4644-8fd4-3a97080476f4",
  //    ],
  //  ]
  const occupations = await fetch("http://localhost:8000/extract-occupations", {
    method: "POST",
    headers: {
      "Content-Type": "application/json",
    },
    body: JSON.stringify(texts),
  });
}

Possible keyword arguments for SkillExtractor

Keyword Argument	Description	Default
skill_threshold	Skills surpassing this cosine similarity threshold are considered a match.	0.6
occupation_threshold	Occupations surpassing this cosine similarity threshold are considered a match.	0.55
device	The device where the copulations will take place. AKA torch device.	"cuda" if available else "cpu"
model	The name of the model to use for embeddings. See available models.	"all-MiniLM-L6-v2"

Notes

If you change a model, don't forget to call the remove_embeddings static method to remove the old embeddings from disk.

SkillExtractor.remove_embeddings()
skill_extractor = SkillExtractor(model="new-model-name")

How it works

1. Generates embeddings for ESCO skills and ISCO occupations.
2. Creates embeddings for the extracted skills.
3. Compares the embeddings of ESCO skills or ISCO occupations with those of the extracted skills using cosine similarity and matches them if the similarity is above a certain threshold.