Update README.md

README.md

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-An mDeBERTa-v3 model trained on English Language News articles by the Executive Approval Project team. This model is trained to detect whether a sequence contains either conflict between
+An mDeBERTa-v3 model fine-tuned on English Language News articles by the Executive Approval Project team. This model is trained to detect whether a sequence contains either conflict between
 political actors, or criticism directed towards a political actor or their policies.
 
-The model was trained for 8 epochs and returned a test-set accuracy of .897 and a balanced accuracy (accounting for the imbalance in the test set, where ~.77 of sequences did not contain conflict)
-of .827. 
+The model was tuned for 8 epochs and returned a test-set accuracy of .897 and a balanced accuracy (accounting for the imbalance in the test set, where ~.77 of sequences did not contain conflict)
+of .827. 
+
+The training/tuning set consisted of 6500 sequences (paragraphs), and a 80/20 train/test split was utilized. Each text was coded 3 times using majority rule.
+
+Cleanlab was utilized to judge label health and fix mislabeled texts, which made up fewer than 10% of all labels. Texts marked as mislabeled were manually verified by a human coder.
+
+The following hyperparameters were used during tuning: 
+    num_train_epochs=8 
+    learning_rate=2e-5
+    per_device_train_batch_size=8 
+    per_device_eval_batch_size=64 
+    warmup_ratio=0.06
+    weight_decay=0.1
+    load_best_model_at_end=True
+    metric_for_best_model="f1_macro
+
+