---
license: cc-by-nc-4.0
language:
- en
metrics:
- f1
pipeline_tag: text-classification
widget:
- text: "Men shouldn’t be making laws about women’s bodies #abortion #Texas"
  example_title: "Statement"
- text: "’Bitter truth’: EU chief pours cold water on idea of Brits keeping EU citizenship after #Brexit HTTPURL via @USER"
  example_title: "Notification"
- text: "Opinion: As the draconian (and then some) abortion law takes effect in #Texas, this is not an idle question for millions of Americans. A slippery slope towards more like-minded Republican state legislatures to try to follow suit. #abortion #F24 HTTPURL"
  example_title: "Reason"
- text: "@USER Blah blah blah blah blah blah"
  example_title: "None"
- text: "republican men and karens make me sick"
  example_title: "Unlabeled 1"
- text: "No empire lives forever! Historical fact! GodWins! 🙏💪🇺🇲"
  example_title: "Unlabeled 2"
- text: "Further author information regarding registration and visa support letters will be sent to the authors soon. #CIKM2023"
  example_title: "Unlabeled 3"
- text: "Ummmmmm"
  example_title: "Unlabeled 4"
- text: "whoever says that The Last Jedi is a good movie is lying or trolling everyone"
  example_title: "Unlabeled 5"
---

# TACO -- Twitter Arguments from COnversations

Introducing TACO, a baseline classification model built upon `AutoModelForSequenceClassification`, designed to identify tweets belonging to four distinct
classes: Reason, Statement, Notification, and None. Tailored specifically for argument mining on Twitter, this baseline model is an evolution of the 
[BERTweet-base](https://huggingface.co/vinai/bertweet-base) architecture, which was originally pre-trained on Twitter data. 
Through fine-tuning with the [TACO dataset](https://doi.org/10.5281/zenodo.8030026), the baseline model acquires its name and excels in the 
extraction of *Twitter Arguments from COnversations*.

## Class Semantics

The TACO framework revolves around the two key elements of an argument, as defined by the [Cambridge Dictionary](https://dictionary.cambridge.org).
It encodes *inference* as *a guess that you make or an opinion that you form based on the information that you have*, and it also leverages the
definition of *information* as *facts or details about a person, company, product, etc.*.

Taken together, the following classes of tweets can be identified by TACO:

* *Statement*, which refers to unique cases where only the *inference* is presented as *something that someone says or writes officially, or an action
done to express an opinion* (see ex. 1).
* *Reason*, which represents a full argument where the *inference* is based on direct *information* mentioned in the tweet, such as a source-reference
or quotation, and thus reveals the author’s motivation *to try to understand and to make judgments based on practical facts* (see ex. 3).
* *Notification*, which refers to a tweet that limits itself to providing *information*, such as media channels promoting their latest articles 
(see ex. 2).
* *None*, a tweet that provides neither *inference* nor *information* (see ex. 4).


In its entirety, TACO can classify the following hierarchy for tweets:

![image](https://www.researchgate.net/profile/Marc-Feger/publication/371595900/figure/fig1/AS:11431281168142295@1686846469455/Hierarchy-of-arguments-with-constituting-elements_W640.jpg)

## Usage

Using this model becomes easy when you have `transformers` installed:

```python
pip install - U transformers
```

Then you can use the model to generate tweet classifications like this:

```python
from transformers import pipeline

pipe = pipeline("text-classification", model="TomatenMarc/TACO")
prediction = pipe("Huggingface is awesome")

print(prediction)
```

<a href="https://github.com/TomatenMarc/TACO/blob/main/notebooks/classifier_cv.ipynb">
    <blockquote style="border-left: 5px solid grey; background-color: #f0f5ff; padding: 10px;">
        Notice: The tweets need to undergo preprocessing before classification.
    </blockquote>
</a>

## Training

The final model underwent training using the entire shuffled ground truth dataset known as TACO, encompassing a total of 1734 tweets.
This dataset showcases the distribution of topics as: #abortion (25.9%), #brexit (29.0%), #got (11.0%), #lotrrop (12.1%), #squidgame (12.7%), and
#twittertakeover (9.3%). For training, we utilized [SimpleTransformers](https://simpletransformers.ai).

Additionally, the category and class distribution of the dataset TACO is as follows:

| Argument     | No-Argument      |
|--------------|------------------|
| 865 (49.88%) | 869 (50.12%)     |

| Reason       | Statement    | Notification | None         |
|--------------|--------------|--------------|--------------|
| 581 (33.50%) | 284 (16.38%) | 500 (28.84%) | 369 (21.28%) |

<p>
    <blockquote style="border-left: 5px solid grey; background-color: #f0f5ff; padding: 10px;">
        Notice: Our training involved TACO to forecast class predictions, where the categories (Argument/No-Argument) represent class aggregations 
based on the inference component.
    </blockquote>
<p>

### Dataloader

```
"data_loader": {
    "type": "torch.utils.data.dataloader.DataLoader",
    "args": {
        "batch_size": 8,
        "sampler": "torch.utils.data.sampler.RandomSampler"
    }
}
```

Parameters of the fit()-Method:

```
{
    "epochs": 5,
    "max_grad_norm": 1,
    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
    "optimizer_params": {
        "lr": 4e-05
    },
    "scheduler": "WarmupLinear",
    "warmup_steps": 66,
    "weight_decay": 0.06
}
```

## Evaluation

We utilized a stratified 10-fold cross-validation approach to present TACO's performance. In doing so, we employed the identical data and parameters
as outlined in the *Training* section. This involved training on k-1 splits and utilizing the kth split for making predictions.

In total, the TACO classifier performs as follows:

### Classification

|             | Precision | Recall  | F1-Score | Support |
|-------------|-----------|---------|----------|---------|
| Reason      | 73.69%    | 75.22%  | 74.45%   | 581     |
| Statement   | 54.37%    | 59.15%  | 56.66%   | 284     |
| Notification| 79.02%    | 77.60%  | 78.30%   | 500     |
| None        | 83.87%    | 77.51%  | 80.56%   | 369     |
|-------------|-----------|---------|----------|---------|
| Accuracy    |           |         | 73.76%   | 1734    |
| Macro Avg   | 72.74%    | 72.37%  | 72.49%   | 1734    |
| Weighted Avg| 74.23%    | 73.76%  | 73.95%   | 1734    |

### Categorization

|             | Precision | Recall  | F1-Score | Support |
|-------------|-----------|---------|----------|---------|
| No-Argument | 86.66%    | 82.97%  | 84.77%   | 869     |
| Argument    | 83.59%    | 87.17%  | 85.34%   | 865     |
|-------------|-----------|---------|----------|---------|
| Accuracy    |           |         | 85.06%   | 1734    |
| Macro Avg   | 85.13%    | 85.07%  | 85.06%   | 1734    |
| Weighted Avg| 85.13%    | 85.06%  | 85.06%   | 1734    |

# Environmental Impact

- **Hardware Type:** A100 PCIe 40GB
- **Hours used:** 10 min
- **Cloud Provider:** [Google Cloud Platform](https://colab.research.google.com)
- **Compute Region:** [asia-southeast1](https://cloud.google.com/compute/docs/gpus/gpu-regions-zones?hl=en) (Singapore)
- **Carbon Emitted:** 0.02kg CO2

## Licensing

[TACO](https://huggingface.co/TomatenMarc/TACO) © 2023 by [Marc Feger](mailto:marc.feger@uni-duesseldorf.de) is licensed under [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/?ref=chooser-v1)

## Contact

If you have any questions, please feel free to reach out to [marc.feger@uni-duesseldorf.de](mailto:marc.feger@uni-duesseldorf.de).