Evaluate predictions with nerval

Based on !340 (merged)

Closes #231 (closed)

dan/ocr/manager/metrics.py

 # -*- coding: utf-8 -*-
 import re
 from collections import defaultdict
+from dataclasses import dataclass
 from operator import attrgetter
 from pathlib import Path
 from typing import Dict, List
@@ -23,6 +24,12 @@ REGEX_ONLY_ONE_SPACE = re.compile(r"\s+")
 METRICS_KEYWORD = {"cer": "chars", "wer": "words", "ner": "tokens"}
 
 
+@dataclass
+class Inference:
+    ground_truth: List[str]
+    prediction: List[str]
+
+
 class MetricManager:
     def __init__(self, metric_names: List[str], dataset_name: str, tokens: Path | None):
         self.dataset_name: str = dataset_name
@@ -40,6 +47,9 @@ class MetricManager:
         self.metric_names: List[str] = metric_names
         self.epoch_metrics = defaultdict(list)
 
+        # List of inferences (prediction with their ground truth)
+        self.inferences = []
+
     def format_string_for_cer(self, text: str, remove_token: bool = False):
         """
         Format string for CER computation: remove layout tokens and extra spaces
@@ -155,6 +165,8 @@ class MetricManager:
             metrics["time"] = [values["time"]]
 
         gt, prediction = values["str_y"], values["str_x"]
+        self.inferences.append(Inference(ground_truth=gt, prediction=prediction))
+
         for metric_name in metric_names:
             match metric_name:
                 case (