Welcome to **RoBERTArg**!

🤖 **Model description**: 
This model was trained on ~40k heterogeneous manually annotated sentences (📚 Stab et al. 2018) of controversial topics (abortion etc.) to classify text into one of two labels: 🏷 **NON-ARGUMENT** (0) and **ARGUMENT** (1).

**Dataset**

Please note that the label distribution in the dataset is imbalanced:
* NON-ARGUMENTS:
* ARGUMENTS:

**Model training**
**RoBERTArg** was fine-tuned on a $RoBERTA_{base}$ pre-trained model using the HuggingFace trainer with the following hyperparameters. The hyperparameters were determined using a hyperparameter search on a 20% validation set.

```
training_args = TrainingArguments(
    num_train_epochs=2,
    learning_rate=2.3102e-06,
    seed=8,
    per_device_train_batch_size=64,
    per_device_eval_batch_size=64,
)
```

**Evaluation**
The model was evaluated using 20% of the sentences (80-20 train-test split).

| Model | Acc | F1 | R arg | R non | P arg | P non |
|----|----|----|----|----|----|----|
| RoBERTArg | 0.8193 | 0.8021 | 0.8463 | 0.7986 | 0.7623 | 0.8719 |

Showing the **confusion matrix** using the 20% of the sentences as an evaluation set:

| | ARGUMENT | NON-ARGUMENT |
|----|----|----|
| ARGUMENT | 2213 | 558 |
| NON-ARGUMENT | 325 | 1790 |

**Intended Uses & Potential Limitations**
The model can be a practical starting point to the complex topic **Argument Mining**. It is a quite challenging task due to the different conceptions of an argument. 

This model is a part of an open-source project providing several models to detect arguments in text.
👉🏾 Check out _chkla/argument-analyzer/_ for more details.

Enjoy and stay tuned! 🚀

📚Stab et al. (2018) https://public.ukp.informatik.tu-darmstadt.de/UKP_Webpage/publications/2018/2018_EMNLP_CS_Cross-topicArgumentMining.pdf