Add attribute "name" to model.py

category_classification/models/allenai__scibert_sci_vocab_uncased/model.py

@@ -1,45 +1,46 @@
-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()
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+
+name = "allenai/scibert_scivocab_uncased"
+
+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()