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, concatenate_datasets, Features, Sequence, ClassLabel, Value, DatasetDict
+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
+
+
+"""
+FEATURES = Features({"tokens": Sequence(feature=Value(dtype='string')), "ner_tags": Sequence(feature=ClassLabel(names=['O', 'B-ORG', 'I-ORG']))})
+
+
+def load_fewnerd():
+    def mapper(sample):
+        sample["ner_tags"] = [int(tag == 5) for tag in sample["ner_tags"]]
+        sample["ner_tags"] = [2 if tag == 1 and idx > 0 and sample["ner_tags"][idx - 1] == 1 else tag for idx, tag in enumerate(sample["ner_tags"])]
+        return sample
+    
+    dataset = load_dataset("DFKI-SLT/few-nerd", "supervised")
+    dataset = dataset.map(mapper, remove_columns=["id", "fine_ner_tags"])
+    dataset = dataset.cast(FEATURES)
+    return dataset
+
+
+def load_conll():
+    label_mapping = {3: 1, 4: 2}
+    def mapper(sample):
+        sample["ner_tags"] = [label_mapping.get(tag, 0) for tag in sample["ner_tags"]]
+        return sample
+
+    dataset = load_dataset("conll2003")
+    dataset = dataset.map(mapper, remove_columns=["id", "pos_tags", "chunk_tags"])
+    dataset = dataset.cast(FEATURES)
+    return dataset
+
+
+def load_ontonotes():
+    label_mapping = {11: 1, 12: 2}
+    def mapper(sample):
+        sample["ner_tags"] = [label_mapping.get(tag, 0) for tag in sample["ner_tags"]]
+        return sample
+
+    dataset = load_dataset("tner/ontonotes5")
+    dataset = dataset.rename_column("tags", "ner_tags")
+    dataset = dataset.map(mapper)
+    dataset = dataset.cast(FEATURES)
+    return dataset
+
+
+def load_multinerd():
+    label_mapping = {5: 1, 6: 2}
+    def mapper(sample):
+        sample["ner_tags"] = [label_mapping.get(tag, 0) for tag in sample["ner_tags"]]
+        return sample
+
+    def lang_filter(sample):
+        return sample["lang"] == "en"
+
+    dataset = load_dataset("Babelscape/multinerd")
+    dataset = dataset.filter(lang_filter)
+    dataset = dataset.map(mapper, remove_columns="lang")
+    dataset = dataset.cast(FEATURES)
+    return dataset
+
+
+def preprocess_raw_dataset(raw_dataset):
+    # Set the number of sentences without an org equal to the number of sentences with an org
+    def has_org(sample):
+        return bool(sum(sample["ner_tags"]))
+    
+    def has_no_org(sample):
+        return not has_org(sample)
+
+    dataset_org = raw_dataset.filter(has_org)
+    dataset_no_org = raw_dataset.filter(has_no_org)
+    dataset_no_org = dataset_no_org.select(random.sample(range(len(dataset_no_org)), k=len(dataset_org)))
+    dataset = concatenate_datasets([dataset_org, dataset_no_org])
+    return dataset
+"""
+
+
+def main() -> None:
+    # Load the dataset, ensure "tokens" and "ner_tags" columns, and get a list of labels
+    labels = ["O", "B-ORG", "I-ORG"]
+    """
+    fewnerd_dataset = load_fewnerd()
+    conll_dataset = load_conll()
+    ontonotes_dataset = load_ontonotes()
+    multinerd_dataset = load_multinerd()
+
+    raw_train_dataset = concatenate_datasets([fewnerd_dataset["train"], conll_dataset["train"], ontonotes_dataset["train"], multinerd_dataset["train"]])
+    raw_eval_dataset = concatenate_datasets([fewnerd_dataset["validation"], conll_dataset["validation"], ontonotes_dataset["validation"], multinerd_dataset["validation"]])
+    raw_test_dataset = concatenate_datasets([fewnerd_dataset["test"], conll_dataset["test"], ontonotes_dataset["test"], multinerd_dataset["test"]])
+
+    train_dataset = preprocess_raw_dataset(raw_train_dataset)
+    eval_dataset = preprocess_raw_dataset(raw_eval_dataset)
+    test_dataset = preprocess_raw_dataset(raw_test_dataset)
+
+    dataset_dict = DatasetDict({
+        "train": train_dataset,
+        "validation": eval_dataset,
+        "test": test_dataset,
+    })
+    dataset_dict.push_to_hub("ner-orgs", private=True)
+    """
+    # breakpoint()
+    dataset = load_dataset("tomaarsen/ner-orgs")
+
+    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 = "bert-base-cased"
+    model_id = f"tomaarsen/span-marker-bert-base-orgs"
+    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,
+            encoder_id=encoder_id,
+            dataset_name="FewNERD, CoNLL2003, OntoNotes v5, and MultiNERD",
+            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=5e-5,
+        per_device_train_batch_size=32,
+        per_device_eval_batch_size=32,
+        num_train_epochs=3,
+        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=100,
+        evaluation_strategy="steps",
+        save_strategy="steps",
+        eval_steps=3000,
+        save_total_limit=1,
+        dataloader_num_workers=4,
+    )
+
+    # 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()