minilm testing

mini_lm.py

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+import json
+import re
+from functools import partial
+from pathlib import Path
+
+import torch
+from datasets import Dataset
+from transformers import (
+    AutoModelForTokenClassification,
+    AutoTokenizer,
+    DataCollatorForTokenClassification,
+    Trainer,
+    TrainingArguments,
+)
+
+from enums import TokenLabel
+
+MODEL_NAME = "microsoft/MiniLM-L12-H384-uncased"
+MODEL_DIR = "./model"
+
+LABEL_LIST = [label.name for label in TokenLabel]
+LABEL2ID = {label: i for i, label in enumerate(LABEL_LIST)}
+ID2LABEL = {i: label for i, label in enumerate(LABEL_LIST)}
+VALUE_TO_NAME = {label.value: label.name for label in TokenLabel}
+
+
+def _normalize_label(label: str) -> str:
+    if label in LABEL2ID:
+        return label
+    if label in VALUE_TO_NAME:
+        return VALUE_TO_NAME[label]
+    raise ValueError(f"Unknown label: {label}")
+
+
+def _text_to_tokens(text: str) -> tuple[list[str], list[tuple[int, int]]]:
+    tokens = []
+    spans = []
+    for match in re.finditer(r"\S+", text):
+        tokens.append(match.group(0))
+        spans.append(match.span())
+    return tokens, spans
+
+
+def _extract_spans(task: dict) -> list[tuple[int, int, str]]:
+    annotations = task.get("annotations") or []
+    if not annotations:
+        return []
+    results = annotations[0].get("result", []) or []
+    spans = []
+    for result in results:
+        value = result.get("value", {})
+        start = value.get("start")
+        end = value.get("end")
+        labels = value.get("labels") or []
+        if start is None or end is None or not labels:
+            continue
+        spans.append((int(start), int(end), _normalize_label(labels[0])))
+    return spans
+
+
+def parse_annotated_dataset(path: str) -> list[dict]:
+    data = json.loads(Path(path).read_text(encoding="utf-8"))
+    if not isinstance(data, list):
+        raise ValueError("Annotated dataset must be a JSON array.")
+
+    examples = []
+    for task in data:
+        text = task["data"]["text"]
+        spans = _extract_spans(task)
+        tokens, token_spans = _text_to_tokens(text)
+        labels = []
+        for token_start, token_end in token_spans:
+            assigned = "RAW_PHRASE"
+            for span_start, span_end, span_label in spans:
+                if token_start < span_end and token_end > span_start:
+                    assigned = span_label
+                    break
+            labels.append(assigned)
+        examples.append({"tokens": tokens, "labels": labels})
+
+    return examples
+
+
+def tokenize_and_align(example, tokenizer, label2id):
+    tokenized = tokenizer(example["tokens"], is_split_into_words=True, truncation=True)
+    word_ids = tokenized.word_ids()
+    labels = []
+    prev = None
+
+    for word_id in word_ids:
+        if word_id is None:
+            labels.append(-100)
+        elif word_id != prev:
+            labels.append(label2id[example["labels"][word_id]])
+        else:
+            labels.append(-100)
+        prev = word_id
+
+    tokenized["labels"] = labels
+    return tokenized
+
+
+def train(dataset_path: str = "./datasets/annotated/ffmpeg_gemini_v1.json", model_dir: str = MODEL_DIR):
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+    model = AutoModelForTokenClassification.from_pretrained(
+        MODEL_NAME, num_labels=len(LABEL_LIST), id2label=ID2LABEL, label2id=LABEL2ID
+    )
+
+    examples = parse_annotated_dataset(dataset_path)
+    dataset = Dataset.from_list(examples)
+    dataset = dataset.map(partial(tokenize_and_align, tokenizer=tokenizer, label2id=LABEL2ID))
+    dataset = dataset.train_test_split(test_size=0.1)
+
+    training_args = TrainingArguments(
+        output_dir=model_dir,
+        learning_rate=3e-5,
+        per_device_train_batch_size=8,
+        num_train_epochs=5,
+        weight_decay=0.01,
+        logging_steps=10,
+        save_strategy="epoch",
+    )
+
+    data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer)
+
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        train_dataset=dataset["train"],
+        eval_dataset=dataset["test"],
+        data_collator=data_collator,
+    )
+
+    trainer.train()
+    trainer.save_model(model_dir)
+    tokenizer.save_pretrained(model_dir)
+
+
+def _load_model(model_dir: str = MODEL_DIR):
+    tokenizer = AutoTokenizer.from_pretrained(model_dir)
+    model = AutoModelForTokenClassification.from_pretrained(model_dir)
+    return tokenizer, model
+
+
+def predict(text: str, model_dir: str = MODEL_DIR, top_k: int = 3):
+    tokenizer, model = _load_model(model_dir)
+    inputs = tokenizer(text.split(), return_tensors="pt", is_split_into_words=True)
+    outputs = model(**inputs)
+
+    logits = outputs.logits[0]
+    probs = torch.softmax(logits, dim=-1)
+    k = min(max(1, top_k), probs.shape[-1])
+    top_probs, top_ids = torch.topk(probs, k, dim=-1)
+    tokens = tokenizer.convert_ids_to_tokens(inputs["input_ids"][0])
+
+    for token, prob_row, id_row in zip(tokens, top_probs, top_ids):
+        preds = [
+            f"{ID2LABEL.get(int(label_id), 'RAW_PHRASE')}:{float(score):.4f}"
+            for label_id, score in zip(id_row, prob_row)
+        ]
+        print(token, ", ".join(preds))
+
+
+if __name__ == "__main__":
+    train()