README.md

---
base_model: distilbert-base-uncased
model-index:
- name: ojobert
  results: []
license: mit
language:
- en
widget:
- text: Would you like to join a major [MASK] company?
tags:
- jobs
---

_Nesta, the UK's innovation agency, has been scraping online job adverts since 2021 and building algorithms to extract and structure information as part of the [Open Jobs Observatory](https://www.nesta.org.uk/project/open-jobs-observatory/) project._ 

_Although we are unable to share the raw data openly, we aim to open source **our models, algorithms and tools** so that anyone can use them for their own research and analysis._

## 📟 About

This model is pre-trained from a `distilbert-base-uncased` checkpoint on 100k sentences from scraped online job postings as part of the Open Jobs Observatory. 

## 🖨️ Use

To use the model:

```
from transformers import pipeline

model = pipeline('fill-mask', model='ihk/ojobert', tokenizer='ihk/ojobert')
```

An example use is as follows:

```

text = "Would you like to join a major [MASK] company?"
results = model(text, top_k=3)

results

>> [{'score': 0.1886572688817978,
  'token': 13859,
  'token_str': 'pharmaceutical',
  'sequence': 'would you like to join a major pharmaceutical company?'},
 {'score': 0.07436735928058624,
  'token': 5427,
  'token_str': 'insurance',
  'sequence': 'would you like to join a major insurance company?'},
 {'score': 0.06400047987699509,
  'token': 2810,
  'token_str': 'construction',
  'sequence': 'would you like to join a major construction company?'}]
```

## ⚖️ Training results

The fine-tuning metrics are as follows:

- eval_loss: 2.5871026515960693
- eval_runtime: 134.4452
- eval_samples_per_second: 14.281
- eval_steps_per_second: 0.223
- epoch: 3.0
- perplexity: 13.29