Update README.md

README.md

@@ -12,7 +12,7 @@ This model was trained on ~25k heterogeneous manually annotated sentences (📚
 
 🗃 **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_.
+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 |
 |----|----|----|
@@ -27,7 +27,7 @@ The dataset (📚 Stab et al. 2018) consists of **ARGUMENTS** (\~11k) that eithe
 
 🏃🏼‍♂️**Model training**
 
-**RoBERTArg** was fine-tuned on a RoBERTA (base) pre-trained model from HuggingFace using the HuggingFace trainer with the following hyperparameters. The hyperparameters were determined using a hyperparameter search on a 20% validation set.
+**RoBERTArg** was fine-tuned on a RoBERTA (base) pre-trained model from HuggingFace using the HuggingFace trainer with the following hyperparameters:
 
 ```
 training_args = TrainingArguments(
@@ -41,13 +41,13 @@ training_args = TrainingArguments(
 
 📊 **Evaluation**
 
-The model was evaluated using 20% of the sentences (80-20 train-test split).
+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 the 20% of the sentences as an evaluation set:
+Showing the **confusion matrix** using again the evaluation set:
 
 | | ARGUMENT | NON-ARGUMENT |
 |----|----|----|
@@ -56,6 +56,6 @@ Showing the **confusion matrix** using the 20% of the sentences as an evaluation
 
 ⚠️ **Intended Uses & Potential Limitations**
 
-The model can be a starting point to dive into the exciting area of argument mining. But be aware. An argument is a complex structure, topic-dependent, and often differs between different text types. Therefore, the model may perform less well on different topics and text types, which are not included in the training set.
+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! 🚀