Update README.md

README.md

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 Welcome to **RoBERTArg**!
 
-🤖 **Training**: Model (RoBERTA_base_) fine-tuned on a heterogeneous argument corpus (~40K sentences, Stab et al. (2018) 📚) corpus of several controversial topics (abortion etc.).
+🤖 **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).
 
-🏷 Using **NON-ARGUMENT** (0) and **ARGUMENT** (1) as labels.
+**Dataset**
 
-**Performance**
+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 |
 
-**Confusion Matrix**
+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! 🚀