utils: add evaluator script

evaluator.py

@@ -0,0 +1,43 @@
+import sys
+
+from datasets import load_dataset
+from transformers import TrainingArguments
+from span_marker import SpanMarkerModel, Trainer
+
+
+# Load the dataset, ensure "tokens" and "ner_tags" columns, and get a list of labels
+dataset = load_dataset("gwlms/germeval2014")
+labels = dataset["train"].features["ner_tags"].feature.names
+
+# Initialize a SpanMarker model using a pretrained BERT-style encoder
+model_name = sys.argv[1]
+model = SpanMarkerModel.from_pretrained(
+    model_name,
+    labels=labels,
+    # SpanMarker hyperparameters:
+    model_max_length=256,
+    marker_max_length=128,
+    entity_max_length=8,
+)
+
+args = TrainingArguments(
+    output_dir="/tmp",
+    per_device_eval_batch_size=64,
+)
+
+# Initialize the trainer using our model, training args & dataset, and train
+trainer = Trainer(
+    model=model,
+    args=args,
+    train_dataset=dataset["train"],
+    eval_dataset=dataset["validation"],
+)
+
+
+print("Evaluating on development set...")
+dev_metrics = trainer.evaluate(dataset["validation"], metric_key_prefix="eval")
+print(dev_metrics)
+
+print("Evaluating on test set...")
+test_metrics = trainer.evaluate(dataset["test"], metric_key_prefix="test")
+print(test_metrics)