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category_classification/models/model.py

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+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+
+
+class SciBertPaperClassifier:
+    def __init__(self, model_path="trained_model"):
+        self.model = AutoModelForSequenceClassification.from_pretrained(model_path)
+        self.tokenizer = AutoTokenizer.from_pretrained(model_path)
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.model.to(self.device)
+        self.model.eval()
+
+    def __call__(self, inputs):
+        texts = [
+            f"AUTHORS: {' '.join(authors) if isinstance(authors, list) else authors} "
+            f"TITLE: {paper['title']} ABSTRACT: {paper['abstract']}"
+            for paper in inputs
+            for authors in [paper.get("authors", "")]
+        ]
+
+        inputs = self.tokenizer(
+            texts, truncation=True, padding=True, max_length=256, return_tensors="pt"
+        ).to(self.device)
+
+        with torch.no_grad():
+            outputs = self.model(**inputs)
+
+        probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
+        scores, labels = torch.max(probs, dim=1)
+
+        return [
+            [{"label": self.model.config.id2label[label.item()], "score": score.item()}]
+            for label, score in zip(labels, scores)
+        ]
+
+    def __getstate__(self):
+        return self.__dict__
+
+    def __setstate__(self, state):
+        self.__dict__ = state
+        self.model.to(self.device)
+
+
+def get_model():
+    return SciBertPaperClassifier()

category_classification/models/train.py

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+from datasets import load_dataset
+from sklearn.metrics import f1_score, accuracy_score
+from transformers import (
+    AutoTokenizer,
+    AutoModelForSequenceClassification,
+    Trainer,
+    TrainingArguments,
+)
+from model import SciBertPaperClassifier
+
+def encode_labels(example):
+    example["labels"] = label2id[example["category"]]
+    return example
+
+def preprocess_function(examples):
+    texts = [
+        f"AUTHORS: {' '.join(a) if isinstance(a, list) else a} TITLE: {t} ABSTRACT: {ab}"
+        for a, t, ab in zip(
+            examples["authors"], examples["title"], examples["abstract"]
+        )
+    ]
+    return tokenizer(texts, truncation=True, padding="max_length", max_length=256)
+
+def compute_metrics(pred):
+    labels = pred.label_ids
+    logits = pred.predictions
+    preds = logits.argmax(-1)
+    return {
+        "accuracy": accuracy_score(labels, preds),
+        "f1": f1_score(labels, preds, average="weighted"),
+    }
+
+if __name__ == "__main__":
+    print("DOWNLOADING DATASET...")
+    data_files = {"train": "arxiv_train.json", "test": "arxiv_test.json"}
+    dataset = load_dataset("json", data_files=data_files)
+
+    dataset["train"] = dataset["train"].shuffle(seed=42).select(range(100000))
+    print(f"DATA IS READY. TRAIN: {len(dataset['train'])}")
+
+    print("LABELING...")
+    unique_labels = sorted(set(example["category"] for example in dataset["train"]))
+    label2id = {label: idx for idx, label in enumerate(unique_labels)}
+    id2label = {idx: label for label, idx in label2id.items()}
+
+    dataset["train"] = dataset["train"].map(encode_labels)
+
+    split_dataset = dataset["train"].train_test_split(test_size=0.1, seed=42)
+    train_dataset = split_dataset["train"]
+    valid_dataset = split_dataset["test"]
+    print(f"TRAIN SET: {len(train_dataset)}, VALIDATION SET: {len(valid_dataset)}")
+
+    print("TOKENIZATION...")
+    model_name = "allenai/scibert_scivocab_uncased"
+    tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
+
+    encoded_train = train_dataset.map(preprocess_function, batched=True, batch_size=32)
+    encoded_valid = valid_dataset.map(preprocess_function, batched=True, batch_size=32)
+    encoded_train.set_format("torch", columns=["input_ids", "attention_mask", "labels"])
+    encoded_valid.set_format("torch", columns=["input_ids", "attention_mask", "labels"])
+    print("TOKENIZATION COMPLETED")
+
+    print("DOWNLOADING MODEL...")
+    model = AutoModelForSequenceClassification.from_pretrained(
+        model_name,
+        num_labels=len(unique_labels),
+        id2label=id2label,
+        label2id=label2id,
+    )
+
+    training_args = TrainingArguments(
+        output_dir="./dataset_output",
+        report_to="none",
+        eval_strategy="steps",
+        eval_steps=100,
+        logging_steps=200,
+        disable_tqdm=True,
+        learning_rate=3e-5,
+        per_device_train_batch_size=32,
+        per_device_eval_batch_size=32,
+        num_train_epochs=2,
+        save_steps=200,
+        fp16=True,
+        remove_unused_columns=False,
+    )
+
+    print("LEARNING...")
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        train_dataset=encoded_train,
+        eval_dataset=encoded_valid,
+        compute_metrics=compute_metrics,
+    )
+    trainer.train()
+    print("LEARNING COMPLETED")
+
+    model.save_pretrained("trained_model")
+    tokenizer.save_pretrained("trained_model")
+
+    print("EVALUATION...")
+    final_metrics = trainer.evaluate()
+    print("METRICS:")
+    for key, value in final_metrics.items():
+        print(f"{key}: {value:.4f}")