Update main readme

README.md

@@ -1,62 +1,106 @@
-# nlp-link
+# 🖇️ NLP Link
 
-A python package to semantically link two lists of texts.
+NLP Link finds the most similar word (or words) in a reference list to an inputted word. For example, if you are trying to find which word is most similar to 'puppies' from a reference list of `['cats', 'dogs', 'rats', 'birds']`, nlp-link will return 'dogs'.
 
-## Set-up
+# 🗺️ SOC Mapper
 
-In setting up this project we ran:
+Another functionality of this package is using the linking methodology to find the [Standard Occupation Classification (SOC)](https://www.ons.gov.uk/methodology/classificationsandstandards/standardoccupationalclassificationsoc) code most similar to an inputted job title. More on this [here](./page1.md).
 
-```
-conda create --name nlp-link pip python=3.9
-conda activate nlp-link
-pip install poetry
-pip install pre-commit black
-pre-commit install
-```
+## 🔨 Usage
 
-```
-poetry init
+Install the package using pip:
 
+```bash
+pip install nlp-link
 ```
 
-```
-poetry install
+### Basic usage
 
-```
+Match two lists in python:
 
-## Usage
+```python
 
-```
 from nlp_link.linker import NLPLinker
 
 nlp_link = NLPLinker()
 
-# dict inputs
-comparison_data = {'a': 'cats', 'b': 'dogs', 'd': 'rats', 'e': 'birds'}
-input_data = {'x': 'owls', 'y': 'feline', 'z': 'doggies', 'za': 'dogs', 'zb': 'chair'}
-nlp_link.load(comparison_data)
-matches = nlp_link.link_dataset(input_data)
-# Top match output
-print(matches)
-
 # list inputs
 comparison_data = ['cats', 'dogs', 'rats', 'birds']
 input_data = ['owls', 'feline', 'doggies', 'dogs','chair']
 nlp_link.load(comparison_data)
 matches = nlp_link.link_dataset(input_data)
 # Top match output
 print(matches)
+
+```
+
+Which outputs:
+
+```
+   input_id input_text  link_id link_text  similarity
+0         0       owls        3     birds    0.613577
+1         1     feline        0      cats    0.669633
+2         2    doggies        1      dogs    0.757443
+3         3       dogs        1      dogs    1.000000
+4         4      chair        0      cats    0.331178
+
+```
+
+### SOC Mapping
+
+Match a list of job titles to SOC codes:
+
+```
+from nlp_link.soc_mapper.soc_map import SOCMapper
+
+soc_mapper = SOCMapper()
+soc_mapper.load()
+job_titles=["data scientist", "Assistant nurse", "Senior financial consultant - London"]
+
+soc_mapper.get_soc(job_titles, return_soc_name=True)
+```
+
+Which will output
+
+```
+[((('2433/04', 'Statistical data scientists'), ('2433', 'Actuaries, economists and statisticians'), '2425'), 'Data scientist'), ((('6131/99', 'Nursing auxiliaries and assistants n.e.c.'), ('6131', 'Nursing auxiliaries and assistants'), '6141'), 'Assistant nurse'), ((('2422/02', 'Financial advisers and planners'), ('2422', 'Finance and investment analysts and advisers'), '3534'), 'Financial consultant')]
+```
+
+## Contributing
+
+The instructions here are for those contrbuting to the repo.
+
+### Set-up
+
+In setting up this project we ran:
+
+```
+conda create --name nlp-link pip python=3.9
+conda activate nlp-link
+pip install poetry
+pip install pre-commit black
+pre-commit install
+```
+
+```
+poetry init
+
+```
+
+```
+poetry install
+
 ```
 
-## Tests
+### Tests
 
 To run tests:
 
 ```
 poetry run pytest tests/
 ```
 
-## Documentation
+### Documentation
 
 Docs for this repo are automatically published to gh-pages branch via. Github actions after a PR is merged into main. We use Material for MkDocs for these. Nothing needs to be done to update these.