language: english
widget:
- text: >-
It has been determined that the amount of greenhouse gases have decreased
by almost half because of the prevalence in the utilization of nuclear
power.
Welcome to RoBERTArg!
🤖 Model description
This model was trained on ~25k heterogeneous manually annotated sentences (📚 Stab et al. 2018) of controversial topics to classify text into one of two labels: 🏷 NON-ARGUMENT (0) and ARGUMENT (1).
🗃 Dataset
The dataset (📚 Stab et al. 2018) consists of ARGUMENTS (\11k) that either support or oppose a topic if it includes a relevant reason for supporting or opposing the topic, or as a NON-ARGUMENT (\14k) if it does not include reasons. The authors focus on controversial topics, i.e., topics that include "an obvious polarity to the possible outcomes" and compile a final set of eight controversial topics: abortion, school uniforms, death penalty, marijuana legalization, nuclear energy, cloning, gun control, and minimum wage.
| TOPIC | ARGUMENT | NON-ARGUMENT |
|---|---|---|
| abortion | 2213 | 2,427 |
| school uniforms | 325 | 1,734 |
| death penalty | 325 | 2,083 |
| marijuana legalization | 325 | 1,262 |
| nuclear energy | 325 | 2,118 |
| cloning | 325 | 1,494 |
| gun control | 325 | 1,889 |
| minimum wage | 325 | 1,346 |
🏃🏼♂️Model training
RoBERTArg was fine-tuned on a RoBERTA (base) pre-trained model from HuggingFace using the HuggingFace trainer with the following hyperparameters:
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 on an evaluation set (20%):
| 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 again the evaluation set:
| ARGUMENT | NON-ARGUMENT | |
|---|---|---|
| ARGUMENT | 2213 | 558 |
| NON-ARGUMENT | 325 | 1790 |
⚠️ Intended Uses & Potential Limitations
The model can only be a starting point to dive into the exciting field of argument mining. But be aware. An argument is a complex structure, with multiple dependencies. Therefore, the model may perform less well on different topics and text types not included in the training set.
Enjoy and stay tuned! 🚀
🐦 Twitter: @chklamm