---
language: en
tags:
- news event detection
- document level
- EventMiner
license: apache-2.0
---

# EventMiner

EventMiner is designed for multilingual news event detection. The goal of news event detection is the automatic extraction of event details from news articles. This event extraction can be done at different levels: document, sentence and word ranging from coarse-granular information to fine-granular information.

We submitted the best results based on EventMiner to [CASE 2021 shared task 1: *Multilingual Protest News Detection*](https://competitions.codalab.org/competitions/31247). Our approach won first place in English for the document level task while ranking within the top four solutions for other languages: Portuguese, Spanish, and Hindi. 

*EventMiner/bigbird-roberta-large-en-doc* is a bigbird-roberta-large sequence classification model fine-tuned on English document level data of the multilingual version of GLOCON gold standard dataset released with [CASE 2021](https://aclanthology.org/2021.case-1.11/). <br>
Labels:
- Label_0: News article does not contain information about a past or ongoing socio-political event
- Label_1: News article contains information about a past or ongoing socio-political event

More details about the training procedure are available with our [codebase](https://github.com/HHansi/EventMiner).

# How to Use
## Load Model
```python
from transformers import BigBirdTokenizer, BigBirdForSequenceClassification

model_name = 'EventMiner/bigbird-roberta-large-en-doc'

tokenizer = BigBirdTokenizer.from_pretrained(model_name)
model = BigBirdForSequenceClassification.from_pretrained(model_name)
```

## Classification
```python
from transformers import pipeline

classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
classifier("Police arrested five more student leaders on Monday when implementing the strike call given by MSU students union as a mark of protest against the decision to introduce payment seats in first-year commerce programme.")
```

# Citation
If you use this model, please consider citing the following paper.

```
@inproceedings{hettiarachchi-etal-2021-daai,
    title = "{DAAI} at {CASE} 2021 Task 1: Transformer-based Multilingual Socio-political and Crisis Event Detection",
    author = "Hettiarachchi, Hansi  and
      Adedoyin-Olowe, Mariam  and
      Bhogal, Jagdev  and
      Gaber, Mohamed Medhat",
    booktitle = "Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.case-1.16",
    doi = "10.18653/v1/2021.case-1.16",
    pages = "120--130",
}
```