Update README.md

README.md

@@ -9,7 +9,65 @@ metrics:
 library_name: transformers
 ---
 
+# Descliption
 
 This model can classify the relation between the sentence pair of input.
 
-Now we are working on preparing the Model card. Please wait for a few days.
+Now we are working on preparing the Model card. Please wait for a few days.
+
+
+The model trained from [bert-large-uncased](https://huggingface.co/bert-large-uncased]) on the dataset published in the paper;[Automatic Prediction of Discourse Connectives](https://arxiv.org/abs/1702.00992).
+
+The dataset to make this model is based on English Wikipedia data and has 20 labels. However, this model will classify into 14 labels. This is because the 20-class data set was restructured to 14 classes to suit our research objective of "automatic slide generation. This distribution is shown below.
+
+
+|Level 1|Level 2|Level 3|Connectives (20)|
+|-------------|-----------------|------------------|--------------------|
+| Temporal    | Synchronous     |                  | meanwhile          |
+| Temporal    | Asynchronous    | Precedence       | then,              |
+| Temporal    | Asynchronous    | Precedence       | finally,           |
+| Temporal    | Asynchronous    | Succession       | by then            |
+| Contingency | Cause           | Result           | therefore          |
+| Comparison  | Concession      | Arg2-as-denier   | however,           |
+| Comparison  | Concession      | Arg2-as-denier   | nevertheless       |
+| Comparison  | Contrast        |                  | on the other hand, |
+| Comparison  | Contrast        |                  | by contrast,       |
+| Expansion   | Conjunction     |                  | and                |
+| Expansion   | Conjunction     |                  | moreover           |
+| Expansion   | Conjunction     |                  | indeed             |
+| Expansion   | Equivalence     |                  | in other words     |
+| Expansion   | Exception       | Arg1-as-excpt    | otherwise          |
+| Expansion   | Instantiation   | Arg2-as-instance | for example,       |
+| Expansion   | Level-of-detail | Arg1-as-detail   | overall,           |
+| Expansion   | Level-of-detail | Arg2-as-detail   | in particular,     |
+| Expansion   | Substitution    | Arg2-as-subst    | instead            |
+| Expansion   | Substitution    | Arg2-as-subst    | rather             |
+
+# Training
+
+The model was trained using AutoModelForSequenceClassification.from_pretrained 
+
+```
+training_args = TrainingArguments(
+    output_dir = output_dir,
+    save_strategy="epoch",
+    num_train_epochs = 5,
+    per_device_train_batch_size=16,
+    per_device_eval_batch_size=32,
+    warmup_steps=0,
+    weight_decay=0.01,
+    logging_dir="./logs",
+    evaluation_strategy="epoch",
+    learning_rate=2e-5,
+    metric_for_best_model="f1",
+    load_best_model_at_end=True
+)
+```
+
+# Evaluation (14 labels and original 20 labels classification) using the dataset test split gives:
+
+|     Model                |     Macro F1    |     Accuracy    |     Precision    |     Recall    |
+|--------------------------|-----------------|-----------------|------------------|---------------|
+|     14 labels classification    |     0.586       |     0.589       |     0.630        |     0.591     |
+|     20 labels classification    |     0.478       |     0.488       |     0.536        |     0.488     |
+