Upload train.py with huggingface_hub

train.py

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+from pathlib import Path
+import random
+import shutil
+from datasets import load_dataset
+from transformers import TrainingArguments
+from span_marker import SpanMarkerModel, Trainer
+from span_marker.model_card import SpanMarkerModelCardData
+from huggingface_hub import upload_folder, upload_file
+
+
+
+def main() -> None:
+    # Load the dataset, ensure "tokens" and "ner_tags" columns, and get a list of labels
+    labels = ["O", "B-ORG", "I-ORG"]
+    dataset_id = "tomaarsen/ner-orgs"
+    dataset = load_dataset(dataset_id)
+
+    train_dataset = dataset["train"]
+    eval_dataset = dataset["validation"]
+    eval_dataset = eval_dataset.select(random.sample(range(len(eval_dataset)), k=3000))
+    test_dataset = dataset["test"]
+
+    # Initialize a SpanMarker model using a pretrained BERT-style encoder
+    encoder_id = "roberta-large"
+    model_id = "nbroad/span-marker-roberta-large-orgs-v1"
+    model = SpanMarkerModel.from_pretrained(
+        encoder_id,
+        labels=labels,
+        # SpanMarker hyperparameters:
+        model_max_length=256,
+        marker_max_length=128,
+        entity_max_length=8,
+        # Model card variables
+        model_card_data=SpanMarkerModelCardData(
+            model_id=model_id,
+            dataset_id=dataset_id,
+            encoder_id=encoder_id,
+            dataset_name="FewNERD, CoNLL2003, and OntoNotes v5",
+            license="cc-by-sa-4.0",
+            language=["en"],
+        ),
+    )
+
+    # Prepare the 🤗 transformers training arguments
+    output_dir = Path("models") / model_id
+    args = TrainingArguments(
+        output_dir=output_dir,
+        run_name=model_id,
+        # Training Hyperparameters:
+        learning_rate=3e-5,
+        per_device_train_batch_size=32,
+        per_device_eval_batch_size=32,
+        num_train_epochs=3,
+        weight_decay=0.01,
+        warmup_ratio=0.05,
+        fp16=True,  # Replace `bf16` with `fp16` if your hardware can't use bf16.
+        # Other Training parameters
+        logging_first_step=True,
+        logging_steps=100,
+        evaluation_strategy="steps",
+        save_strategy="steps",
+        eval_steps=600,
+        save_total_limit=1,
+        dataloader_num_workers=4,
+        metric_for_best_model="overall_f1",
+        greater_is_better=True,
+    )
+
+    # 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()
+
+    # Compute & save the metrics on the test set
+    metrics = trainer.evaluate(test_dataset, metric_key_prefix="test")
+    trainer.save_metrics("test", metrics)
+
+    # Save the model & training script locally
+    trainer.save_model(output_dir / "checkpoint-final")
+    shutil.copy2(__file__, output_dir / "checkpoint-final" / "train.py")
+
+    # Upload everything to the Hub
+    breakpoint()
+    model.push_to_hub(model_id, private=True)
+    upload_folder(folder_path=output_dir / "runs", path_in_repo="runs", repo_id=model_id)
+    upload_file(path_or_fileobj=__file__, path_in_repo="train.py", repo_id=model_id)
+    upload_file(path_or_fileobj=output_dir / "all_results.json", path_in_repo="all_results.json", repo_id=model_id)
+    upload_file(path_or_fileobj=output_dir / "emissions.csv", path_in_repo="emissions.csv", repo_id=model_id)
+
+
+if __name__ == "__main__":
+    main()