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train.py

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+from datasets import load_dataset
+from transformers import TrainingArguments
+
+from span_marker import SpanMarkerModel, Trainer
+
+
+def main() -> None:
+    # Load the dataset, ensure "tokens" and "ner_tags" columns, and get a list of labels
+    dataset = "Babelscape/multinerd"
+    train_dataset = load_dataset(dataset, split="train")
+    eval_dataset = load_dataset(dataset, split="validation").shuffle().select(range(3000))
+    labels = [
+        "O",
+        "B-PER",
+        "I-PER",
+        "B-ORG",
+        "I-ORG",
+        "B-LOC",
+        "I-LOC",
+        "B-ANIM",
+        "I-ANIM",
+        "B-BIO",
+        "I-BIO",
+        "B-CEL",
+        "I-CEL",
+        "B-DIS",
+        "I-DIS",
+        "B-EVE",
+        "I-EVE",
+        "B-FOOD",
+        "I-FOOD",
+        "B-INST",
+        "I-INST",
+        "B-MEDIA",
+        "I-MEDIA",
+        "B-MYTH",
+        "I-MYTH",
+        "B-PLANT",
+        "I-PLANT",
+        "B-TIME",
+        "I-TIME",
+        "B-VEHI",
+        "I-VEHI",
+    ]
+
+    # Initialize a SpanMarker model using a pretrained BERT-style encoder
+    model_name = "xlm-roberta-base"
+    model = SpanMarkerModel.from_pretrained(
+        model_name,
+        labels=labels,
+        # SpanMarker hyperparameters:
+        model_max_length=256,
+        marker_max_length=128,
+        entity_max_length=6,
+    )
+
+    # Prepare the 🤗 transformers training arguments
+    args = TrainingArguments(
+        output_dir="models/span_marker_xlm_roberta_base_multinerd",
+        # Training Hyperparameters:
+        learning_rate=1e-5,
+        per_device_train_batch_size=32,
+        per_device_eval_batch_size=32,
+        # gradient_accumulation_steps=2,
+        num_train_epochs=1,
+        weight_decay=0.01,
+        warmup_ratio=0.1,
+        bf16=True,  # Replace `bf16` with `fp16` if your hardware can't use bf16.
+        # Other Training parameters
+        logging_first_step=True,
+        logging_steps=50,
+        evaluation_strategy="steps",
+        save_strategy="steps",
+        eval_steps=1000,
+        save_total_limit=2,
+        dataloader_num_workers=2,
+    )
+
+    # Initialize the trainer using our model, training args & dataset, and train
+    trainer = Trainer(
+        model=model,
+        args=args,
+        train_dataset=train_dataset,
+        eval_dataset=eval_dataset,
+    )
+    trainer.train()
+    trainer.save_model("models/span_marker_xlm_roberta_base_multinerd/checkpoint-final")
+
+    test_dataset = load_dataset(dataset, split="test")
+    # Compute & save the metrics on the test set
+    metrics = trainer.evaluate(test_dataset, metric_key_prefix="test")
+    trainer.save_metrics("test", metrics)
+
+
+if __name__ == "__main__":
+    main()
+
+"""
+This SpanMarker model will ignore 2.239322% of all annotated entities in the train dataset. This is caused by the SpanMarkerModel maximum entity length of 6 words and the maximum model input length of 256 tokens.
+These are the frequencies of the missed entities due to maximum entity length out of 4111958 total entities:
+- 35814 missed entities with 7 words (0.870972%)
+- 21246 missed entities with 8 words (0.516688%)
+- 12680 missed entities with 9 words (0.308369%)
+- 7308 missed entities with 10 words (0.177726%)
+- 4414 missed entities with 11 words (0.107345%)
+- 2474 missed entities with 12 words (0.060166%)
+- 1894 missed entities with 13 words (0.046061%)
+- 1130 missed entities with 14 words (0.027481%)
+- 744 missed entities with 15 words (0.018094%)
+- 582 missed entities with 16 words (0.014154%)
+- 344 missed entities with 17 words (0.008366%)
+- 226 missed entities with 18 words (0.005496%)
+- 84 missed entities with 19 words (0.002043%)
+- 46 missed entities with 20 words (0.001119%)
+- 20 missed entities with 21 words (0.000486%)
+- 20 missed entities with 22 words (0.000486%)
+- 12 missed entities with 23 words (0.000292%)
+- 18 missed entities with 24 words (0.000438%)
+- 2 missed entities with 25 words (0.000049%)
+- 4 missed entities with 26 words (0.000097%)
+- 4 missed entities with 27 words (0.000097%)
+- 2 missed entities with 31 words (0.000049%)
+- 8 missed entities with 32 words (0.000195%)
+- 6 missed entities with 33 words (0.000146%)
+- 2 missed entities with 34 words (0.000049%)
+- 4 missed entities with 36 words (0.000097%)
+- 8 missed entities with 37 words (0.000195%)
+- 2 missed entities with 38 words (0.000049%)
+- 2 missed entities with 41 words (0.000049%)
+- 2 missed entities with 72 words (0.000049%)
+Additionally, a total of 2978 (0.072423%) entities were missed due to the maximum input length.
+
+This SpanMarker model won't be able to predict 2.501087% of all annotated entities in the evaluation dataset. This is caused by the SpanMarkerModel maximum entity length of 6 words.
+These are the frequencies of the missed entities due to maximum entity length out of 4598 total entities:
+- 45 missed entities with 7 words (0.978686%)
+- 27 missed entities with 8 words (0.587212%)
+- 21 missed entities with 9 words (0.456720%)
+- 9 missed entities with 10 words (0.195737%)
+- 3 missed entities with 12 words (0.065246%)
+- 4 missed entities with 13 words (0.086994%)
+- 3 missed entities with 14 words (0.065246%)
+- 1 missed entities with 15 words (0.021749%)
+- 1 missed entities with 16 words (0.021749%)
+- 1 missed entities with 20 words (0.021749%)
+"""
+
+"""
+wandb: Run summary:
+wandb:                      eval/loss 0.00594
+wandb:          eval/overall_accuracy 0.98181
+wandb:                eval/overall_f1 0.90333
+wandb:         eval/overall_precision 0.91259
+wandb:            eval/overall_recall 0.89427
+wandb:                   eval/runtime 21.4308
+wandb:        eval/samples_per_second 154.171
+wandb:          eval/steps_per_second 4.853
+wandb:                      test/loss 0.00559
+wandb:          test/overall_accuracy 0.98247
+wandb:                test/overall_f1 0.91314
+wandb:         test/overall_precision 0.91994
+wandb:            test/overall_recall 0.90643
+wandb:                   test/runtime 2202.6894
+wandb:        test/samples_per_second 169.652
+wandb:          test/steps_per_second 5.302
+wandb:                    train/epoch 1.0
+wandb:              train/global_step 93223
+wandb:            train/learning_rate 0.0
+wandb:                     train/loss 0.0049
+wandb:               train/total_flos 7.851073325660897e+17
+wandb:               train/train_loss 0.01782
+wandb:            train/train_runtime 41756.9748
+wandb: train/train_samples_per_second 71.44
+wandb:   train/train_steps_per_second 2.233
+"""