is_info is_motivation is_neutral is_referral is_strategy is_tip
---
language: en
license: mit
---

# Uganda Labor Market Interview Text Classification

This model is a fine-tuned [Roberta base model](https://huggingface.co/roberta-base) using text transcripts of interviews between Vocational Training Institutes (VTI) students and their successful alumni in Uganda on the subject of the labor market.

## Model description

There are 6 categories in total. In the training data, a sentence can get classified as more than one topic. I classify a sentence using the following criteria:  

info: information about the job market, working conditions, salaries, and what to expect at work. Also alumn's and student's current situation in the job market, career plans, and past experience. Note if the alumn mentions using strategies in her/his experience, I also classify the sentence as a strategy.  

tip: tips for how to behave and improve ourselves while at work. The majority of tips involve being disciplined, humble, treating colleagues and clients well so that you can learn, and not involving in illegal stuff. If the alumni mention doing so increases the chance of getting jobs, I also classify the sentence as a strategy.  

strategy: tips that help students get a better chance of getting hired or getting a better job. Including how to search for companies, what kind of companies to apply for, how to write and submit applications, when and how many companies to apply for, how to behave during interviews, how to get jobs through different channels, and making and maintaining connections, and general advice on how to improve job-related abilities. Also tips for starting your own business, including saving for capital, finding locations, business models, purchasing apparatuses, and attracting and treating clients.  

motivation: General advice of being confident, patient, persistent, engaged, optimistic, etc in the job market. Note if the alumni mention that advice in a particular context, for example "during an interview you need to show that you are a patient person," or "when doing your work you need to be patient," I will also classify these sentences as strategy and tip respectively.  

referral: referring students to companies and individuals, or affirmative answers to the student's request for connection.  

neutral: Introductions, exchanging contacts, pure technical stuff, conversations about school or exams that are not related to getting jobs, miscellaneous conversations that do not belong to the 5 topics above, and those whose meaning is unclear due to language improficiency or translation issues.   


### How to use

You can use this model directly with a pipeline for text classification:

```python
>>> from transformers import pipeline
>>> pipe = pipeline("text-classification", model= "wanghao2023/uganda-labor-market-interview-text-classification", tokenizer = "wanghao2023/uganda-labor-market-interview-text-classification", return_all_scores = True)
>>> pipe("if they think you know too much, they won't teach you.")
[[{'label': 'is_info', 'score': 0.18128268420696259},
  {'label': 'is_tip', 'score': 0.5684323310852051},
  {'label': 'is_strategy', 'score': 0.22818608582019806},
  {'label': 'is_motivation', 'score': 0.03250108286738396},
  {'label': 'is_neutral', 'score': 0.05972086638212204},
  {'label': 'is_referral', 'score': 0.013502764515578747}]]

```

### Limitations and bias

The classification of a sentence is heavily based on the context. For example, "be patient" can be classified as tip and/or strategy and/or motivation depending on which occasion the alumna asks the students to be patient. If the alumna asks the student to be patient during the interview, it's strategy; if the alumna asks the student to be patient while at work, then it's tip; if no specific context is given, then it's motivation.  

## Evaluation results

This model achieves the following results when tested on the validation dataset (multilabel, threshold = 0.3). There is a huge room for improvement but it performs much better than a dice roll at least:

| F1 | Roc Auc | Accuracy  |
|:----:|:----:|:----:|
| 0.655779 | 0.799979 | 0.552670 |