Add model for training

bert_paper_classifier_model.py

@@ -0,0 +1,41 @@
+import typing as tp
+
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+
+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):
+        if not isinstance(inputs, tp.Iterable):
+            inputs = [inputs]
+        texts = [
+            f"AUTHORS: {' '.join(paper.authors) if isinstance(paper.authors, list) else paper.authors} "
+            f"TITLE: {paper.title} ABSTRACT: {paper.abstract}"
+            for paper in inputs
+        ]
+
+        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)
+        results = []
+        for prob in probs:
+            result = [
+                {self.model.config.id2label[label_idx]: score.item()}
+                for label_idx, score in enumerate(prob)
+            ]
+            results.append(result)
+        if 1 == len(results):
+            return results[0]
+        return results