utils: add trainer script

trainer.py

@@ -0,0 +1,61 @@
+from datasets import load_dataset
+from transformers import TrainingArguments
+from span_marker import SpanMarkerModel, Trainer
+
+def perform_training(learning_rate: float, seed: int) -> None:
+    # 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 = "gwlms/span-marker-bert-germeval14"
+    model = SpanMarkerModel.from_pretrained(
+        model_name,
+        labels=labels,
+        # SpanMarker hyperparameters:
+        model_max_length=256,
+        marker_max_length=128,
+        entity_max_length=8,
+    )
+
+    # Prepare the 🤗 transformers training arguments
+    args = TrainingArguments(
+        output_dir=f"./span_marker-{model_name}-bs16-lr{learning_rate}-{seed}",
+        # Training Hyperparameters:
+        learning_rate=learning_rate,
+        per_device_train_batch_size=16,
+        per_device_eval_batch_size=16,
+        num_train_epochs=3,
+        weight_decay=0.01,
+        warmup_ratio=0.1,
+        fp16=True,  # Replace `bf16` with `fp16` if your hardware can't use bf16.
+        # Other Training parameters
+        logging_first_step=True,
+        logging_steps=50,
+        evaluation_strategy="epoch",
+        save_strategy="epoch",
+        save_total_limit=11,
+        dataloader_num_workers=2,
+        seed=seed,
+        load_best_model_at_end=True,
+    )
+
+    # 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"],
+    )
+    trainer.train()
+    trainer.save_model(f"./span_marker-{model_name}-bs16-lr{learning_rate}-{seed}/best-checkpoint")
+
+    # Compute & save the metrics on the test set
+    metrics = trainer.evaluate(dataset["test"], metric_key_prefix="test")
+    trainer.save_metrics("test", metrics)
+
+
+if __name__ == "__main__":
+    for learning_rate in [5e-05]:
+        for seed in [1,2,3,4,5]:
+            perform_training(learning_rate, seed)