Create training/utils.py

training/utils.py

@@ -0,0 +1,29 @@
+from typing import List, Dict
+import numpy as np
+import evaluate
+
+def compute_metrics_sentiment(eval_pred):
+    logits, labels = eval_pred
+    preds = np.argmax(logits, axis=-1)
+    acc = (preds == labels).mean().item()
+    return {"accuracy": acc}
+
+def compute_metrics_ner(eval_pred, label_list: List[str]):
+    seqeval = evaluate.load("seqeval")
+    logits, labels = eval_pred
+    preds = logits.argmax(-1)
+    true_preds = [
+        [label_list[p] for (p, l) in zip(pred, lab) if l != -100]
+        for pred, lab in zip(preds, labels)
+    ]
+    true_labels = [
+        [label_list[l] for (p, l) in zip(pred, lab) if l != -100]
+        for pred, lab in zip(preds, labels)
+    ]
+    results = seqeval.compute(predictions=true_preds, references=true_labels)
+    return {
+        "precision": results["overall_precision"],
+        "recall": results["overall_recall"],
+        "f1": results["overall_f1"],
+        "accuracy": results["overall_accuracy"],
+    }