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README.md
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language:
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- 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."
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### Welcome to RoBERTArg!
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🤖 **Model description**
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This model was trained on ~25k heterogeneous manually annotated sentences (📚 [Stab et al. 2018](https://www.aclweb.org/anthology/D18-1402/)) of controversial topics to classify text into one of two labels: 🏷 **NON-ARGUMENT** (0) and **ARGUMENT** (1).
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🗃 **Dataset**
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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_.
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| TOPIC | ARGUMENT | NON-ARGUMENT |
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|----|----|----|
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| abortion | 2213 | 2,427 |
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| school uniforms | 325 | 1,734 |
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| death penalty | 325 | 2,083 |
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| marijuana legalization | 325 | 1,262 |
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| nuclear energy | 325 | 2,118 |
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| cloning | 325 | 1,494 |
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| gun control | 325 | 1,889 |
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| minimum wage | 325 | 1,346 |
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🏃🏼♂️**Model training**
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**RoBERTArg** was fine-tuned on a RoBERTA (base) pre-trained model from HuggingFace using the HuggingFace trainer with the following hyperparameters:
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```
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training_args = TrainingArguments(
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num_train_epochs=2,
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learning_rate=2.3102e-06,
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seed=8,
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per_device_train_batch_size=64,
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per_device_eval_batch_size=64,
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)
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```
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📊 **Evaluation**
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The model was evaluated on an evaluation set (20%):
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| Model | Acc | F1 | R arg | R non | P arg | P non |
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|----|----|----|----|----|----|----|
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| RoBERTArg | 0.8193 | 0.8021 | 0.8463 | 0.7986 | 0.7623 | 0.8719 |
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Showing the **confusion matrix** using again the evaluation set:
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| | ARGUMENT | NON-ARGUMENT |
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|----|----|----|
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| ARGUMENT | 2213 | 558 |
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| NON-ARGUMENT | 325 | 1790 |
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⚠️ **Intended Uses & Potential Limitations**
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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.
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Enjoy and stay tuned! 🚀
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🐦 Twitter: [@chklamm](http://twitter.com/chklamm)
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language: en
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