setup models

adapters/gliner/adapter.py

@@ -0,0 +1,55 @@
+"""Adaptador do GLiNER2.5-multi-Decide via classify_text (tarefa 44).
+
+O GLiNER devolve só o rótulo vencedor no modo de rótulo único. Para obter a
+distribuição completa, cada pergunta é enviada com `multi_label=True`,
+`class_act="softmax"` e `cls_threshold=0`: o modelo calcula o mesmo softmax do modo
+de rótulo único e devolve todas as opções com sua probabilidade.
+"""
+from __future__ import annotations
+
+from dmb.adapter import Adapter
+from dmb.jev import ContextLimit, answer_from_distribution, option_descriptions
+
+
+class GlinerAdapter(Adapter):
+    name = "gliner"
+    probabilities_source = "native"
+
+    def load(self) -> None:
+        from gliner2 import AutoExtractor
+        from tokenizers import Tokenizer
+
+        self.model = AutoExtractor.from_pretrained(str(self.model_dir))
+        if self.device not in ("auto", "cpu"):
+            self.model.to(self.device)
+        elif self.device == "auto":
+            import torch
+            if torch.cuda.is_available():
+                self.model.to("cuda")
+        self.model.eval()
+        self.tokenizer = Tokenizer.from_file(str(self.model_dir / "tokenizer.json"))
+
+    def native_context_limit(self) -> int:
+        return int(getattr(self.model.config, "max_len", 512) or 512)
+
+    def count_tokens(self, text: str) -> int:
+        return len(self.tokenizer.encode(text, add_special_tokens=False).ids)
+
+    def decide(self, state: str, questions: dict) -> tuple[dict, int]:
+        tokens = self.count_tokens(state)
+        if tokens > self.context_limit:
+            raise ContextLimit(f"{tokens} tokens > limite {self.context_limit}")
+        # As instruções entram como prefixo do texto: o classify_text não tem campo de pergunta.
+        answers = {}
+        for name, question in questions.items():
+            labels = option_descriptions(question)
+            text = f"Pergunta: {question['instructions']}\n{state}"
+            raw = self.model.classify_text(
+                text,
+                {name: {"labels": labels, "multi_label": True, "class_act": "softmax",
+                        "cls_threshold": 0.0}},
+                include_confidence=True,
+            )[name]
+            dist = {item["label"]: item["confidence"] for item in raw}
+            answers[name] = answer_from_distribution(question, dist)
+        return answers, tokens