PSE2_FF
/
pse2_ff
0
0
Fork 0
Code Issues Pull Requests Packages Projects 1 Releases Wiki Activity

Kommentare aus dem Review berücksichtigt und umgesetzt

pull/94/head
Abdulrahman Dabbagh 2025-06-29 00:28:04 +02:00
parent 15e7752e54
commit 09a3099584
19 changed files with 65 additions and 1158 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

63
project/backend/coordinator/controller/spacy_controller.py
View File

@ -5,21 +5,17 @@ import puremagic
from werkzeug.utils import secure_filename
from model.database import db
import os
import json
import requests
spacy_controller = Blueprint("spacy", __name__, url_prefix="/api/spacy")
SPACY_TRAINING_URL = os.getenv("SPACY_TRAINING_URL", "http://spacy:5052/train")
training_running_flag_path = os.path.join("spacy_training", "training_running.json")
SPACY_URL = os.getenv("SPACY_URL", "http://spacy:5052")
@spacy_controller.route("/train", methods=["POST"])
def trigger_training():
try:
with open(training_running_flag_path, "w") as f:
json.dump({"running": True}, f)
response = requests.post(SPACY_TRAINING_URL, timeout=600)
if response.ok:
return jsonify({"message": "Training erfolgreich angestoßen."}), 200
@ -109,48 +105,29 @@ def delete_file(id):
@spacy_controller.route("/append-training-entry", methods=["POST"])
def append_training_entry():
def forward_training_entry():
entry = request.get_json()
if not entry or "text" not in entry or "entities" not in entry:
return (
jsonify(
{"error": "Ungültiges Format – 'text' und 'entities' erforderlich."}
),
400,
)
path = os.path.join("spacy_training", "annotation_data.json")
try:
os.makedirs(os.path.dirname(path), exist_ok=True)
if os.path.exists(path):
with open(path, "r", encoding="utf-8") as f:
data = json.load(f)
else:
data = []
if entry in data:
return jsonify({"message": "Eintrag existiert bereits."}), 200
data.append(entry)
with open(path, "w", encoding="utf-8") as f:
json.dump(data, f, indent=2, ensure_ascii=False)
return jsonify({"message": "Eintrag erfolgreich gespeichert."}), 200
response = requests.post(f"{SPACY_URL}/append-training-entry", json=entry)
return jsonify(response.json()), response.status_code
except Exception as e:
print(f"[ERROR] Fehler beim Schreiben: {e}")
return jsonify({"error": "Interner Fehler beim Schreiben."}), 500
return jsonify({"error": str(e)}), 500
# globale Variable oben einfügen
current_training_status = {"running": False}
@spacy_controller.route("/training/status", methods=["POST"])
def update_training_status():
data = request.get_json()
current_training_status["running"] = data.get("running", False)
running = current_training_status["running"]
print(f"[INFO] Trainingsstatus aktualisiert: running = {running}")
return jsonify({"status": "success", "running": current_training_status["running"]})
@spacy_controller.route("/train-status", methods=["GET"])
def training_status():
try:
if os.path.exists(training_running_flag_path):
with open(training_running_flag_path, "r") as f:
status = json.load(f)
return jsonify(status), 200
else:
return jsonify({"running": False}), 200
except Exception as e:
return jsonify({"error": "Fehler beim Statuscheck", "details": str(e)}), 500
return jsonify(current_training_status), 200

8
project/backend/exxetaGPT-service/Dockerfile
View File

@ -1,8 +0,0 @@
FROM python:3.11-slim
WORKDIR /app
COPY . /app
RUN pip install --no-cache-dir -r requirements.txt
ENV PYTHONUNBUFFERED=1
CMD ["python", "extractExxeta.py"]

40
project/backend/spacy-service/app.py
View File

@ -14,7 +14,7 @@ training_status = {"running": False}
app = Flask(__name__)
CORS(app)
COORDINATOR_URL = os.getenv("COORDINATOR_URL", "http://coordinator:5000")
VALIDATE_SERVICE_URL = os.getenv(
"VALIDATE_SERVICE_URL", "http://localhost:5054/validate"
)
@ -88,31 +88,11 @@ def append_training_entry():
@app.route("/train", methods=["POST"])
def trigger_training():
from threading import Thread
import subprocess
import shutil
def run_training():
training_status["running"] = True
try:
if os.path.exists("output/model-last"):
shutil.copytree(
"output/model-last", "output/model-backup", dirs_exist_ok=True
)
subprocess.run(["python", "spacy_training/ner_trainer.py"], check=True)
load_model()
except Exception as e:
print("Training failed:", e)
training_status["running"] = False
Thread(target=run_training).start()
return jsonify({"message": "Training gestartet"}), 200
@app.route("/train-status", methods=["GET"])
def get_training_status():
return jsonify(training_status), 200
@app.route("/reload-model", methods=["POST"])
def reload_model():
try:
@ -127,16 +107,32 @@ def reload_model():
def run_training():
training_status["running"] = True
notify_coordinator(True)
try:
if os.path.exists("output/model-last"):
shutil.copytree(
"output/model-last", "output/model-backup", dirs_exist_ok=True
)
subprocess.run(["python", "spacy_training/ner_trainer.py"], check=True)
load_model() # ⬅ Modell nach dem Training direkt neu laden
load_model()
except Exception as e:
print("Training failed:", e)
training_status["running"] = False
notify_coordinator(False)
def notify_coordinator(running: bool):
try:
response = requests.post(
f"{COORDINATOR_URL}/api/spacy/training/status", json={"running": running}
)
print(
f"[SPACY] Coordinator: running = {running}, Status = {response.status_code}"
)
except Exception as e:
print(f"[SPACY] Fehler beim Senden des Trainingsstatus: {e}")
if __name__ == "__main__":

180
project/backend/spacy-service/spacy_training/annotation_data.json
View File

@ -1638,185 +1638,5 @@
"RENDITE"
]
]
},
{
"text": "Die Gesamtrendite beträgt 7,2 %.",
"entities": [
[
1,
5,
"NEUEKENNZAHL"
]
]
},
{
"text": "fhfhfh56",
"entities": [
[
6,
8,
"TEST545"
]
]
},
{
"text": "fhfhfh56",
"entities": [
[
6,
8,
"TEST345"
]
]
},
{
"text": "sdgds45",
"entities": [
[
6,
7,
"TEST243"
]
]
},
{
"text": "4t4r3",
"entities": [
[
4,
5,
"TEST243"
]
]
},
{
"text": "sdgds45",
"entities": [
[
6,
7,
"DGTDDTFHZ"
]
]
},
{
"text": "gjufzj45",
"entities": [
[
7,
8,
"DGTDDTFHZ"
]
]
},
{
"text": "irr beträgt 43",
"entities": [
[
12,
14,
"TEST3243"
]
]
},
{
"text": "irr beträgt 43",
"entities": [
[
12,
14,
"IRR"
]
]
},
{
"text": "Rendite besträgt 5 %",
"entities": [
[
17,
20,
"RENDITE"
]
]
},
{
"text": "RenditeX besträgt 5 %",
"entities": [
[
18,
21,
"RENDITE_X"
]
]
},
{
"text": "gtg3ahz8",
"entities": [
[
7,
8,
"ERTRETT"
]
]
},
{
"text": "wffwee 45",
"entities": [
[
7,
9,
"TEST45"
]
]
},
{
"text": "efwwef 45",
"entities": [
[
7,
9,
"TEST12"
]
]
},
{
"text": "wfwefwe34",
"entities": [
[
7,
9,
"TEST232"
]
]
},
{
"text": "fwefbmj34",
"entities": [
[
7,
9,
"TEST223"
]
]
},
{
"text": "asdas45",
"entities": [
[
5,
7,
"TEST122"
]
]
},
{
"text": "ewefw4",
"entities": [
[
5,
6,
"TEST3434"
]
]
}
]

85
project/backend/spacy-service/spacy_training/base_config.cfg
View File

@ -1,85 +0,0 @@
# This is an auto-generated partial config. To use it with 'spacy train'
# you can run spacy init fill-config to auto-fill all default settings:
# python -m spacy init fill-config ./base_config.cfg ./config.cfg
[paths]
train = ./data/train.spacy
dev = ./data/train.spacy
vectors = null
[system]
gpu_allocator = null
[nlp]
lang = "de"
pipeline = ["tok2vec","ner"]
batch_size = 1000
[components]
[components.tok2vec]
factory = "tok2vec"
[components.tok2vec.model]
@architectures = "spacy.Tok2Vec.v2"
[components.tok2vec.model.embed]
@architectures = "spacy.MultiHashEmbed.v2"
width = ${components.tok2vec.model.encode.width}
attrs = ["NORM", "PREFIX", "SUFFIX", "SHAPE"]
rows = [5000, 1000, 2500, 2500]
include_static_vectors = false
[components.tok2vec.model.encode]
@architectures = "spacy.MaxoutWindowEncoder.v2"
width = 96
depth = 4
window_size = 1
maxout_pieces = 3
[components.ner]
factory = "ner"
[components.ner.model]
@architectures = "spacy.TransitionBasedParser.v2"
state_type = "ner"
extra_state_tokens = false
hidden_width = 64
maxout_pieces = 2
use_upper = true
nO = null
[components.ner.model.tok2vec]
@architectures = "spacy.Tok2VecListener.v1"
width = ${components.tok2vec.model.encode.width}
[corpora]
[corpora.train]
@readers = "spacy.Corpus.v1"
path = ${paths.train}
max_length = 0
[corpora.dev]
@readers = "spacy.Corpus.v1"
path = ${paths.dev}
max_length = 0
[training]
dev_corpus = "corpora.dev"
train_corpus = "corpora.train"
[training.optimizer]
@optimizers = "Adam.v1"
[training.batcher]
@batchers = "spacy.batch_by_words.v1"
discard_oversize = false
tolerance = 0.2
[training.batcher.size]
@schedules = "compounding.v1"
start = 100
stop = 1000
compound = 1.001
[initialize]
vectors = ${paths.vectors}

145
project/backend/spacy-service/spacy_training/config.cfg
View File

@ -1,145 +0,0 @@
[paths]
train = "./data/train.spacy"
dev = "./data/train.spacy"
vectors = null
init_tok2vec = null
[system]
gpu_allocator = null
seed = 0
[nlp]
lang = "de"
pipeline = ["tok2vec","ner"]
batch_size = 1000
disabled = []
before_creation = null
after_creation = null
after_pipeline_creation = null
tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
vectors = {"@vectors":"spacy.Vectors.v1"}
[components]
[components.ner]
factory = "ner"
incorrect_spans_key = null
moves = null
scorer = {"@scorers":"spacy.ner_scorer.v1"}
update_with_oracle_cut_size = 100
[components.ner.model]
@architectures = "spacy.TransitionBasedParser.v2"
state_type = "ner"
extra_state_tokens = false
hidden_width = 64
maxout_pieces = 2
use_upper = true
nO = null
[components.ner.model.tok2vec]
@architectures = "spacy.Tok2VecListener.v1"
width = ${components.tok2vec.model.encode.width}
upstream = "*"
[components.tok2vec]
factory = "tok2vec"
[components.tok2vec.model]
@architectures = "spacy.Tok2Vec.v2"
[components.tok2vec.model.embed]
@architectures = "spacy.MultiHashEmbed.v2"
width = ${components.tok2vec.model.encode.width}
attrs = ["NORM","PREFIX","SUFFIX","SHAPE"]
rows = [5000,1000,2500,2500]
include_static_vectors = false
[components.tok2vec.model.encode]
@architectures = "spacy.MaxoutWindowEncoder.v2"
width = 96
depth = 4
window_size = 1
maxout_pieces = 3
[corpora]
[corpora.dev]
@readers = "spacy.Corpus.v1"
path = ${paths.dev}
max_length = 0
gold_preproc = false
limit = 0
augmenter = null
[corpora.train]
@readers = "spacy.Corpus.v1"
path = ${paths.train}
max_length = 0
gold_preproc = false
limit = 0
augmenter = null
[training]
dev_corpus = "corpora.dev"
train_corpus = "corpora.train"
seed = ${system.seed}
gpu_allocator = ${system.gpu_allocator}
dropout = 0.1
accumulate_gradient = 1
patience = 1600
max_epochs = 0
max_steps = 20000
eval_frequency = 200
frozen_components = []
annotating_components = []
before_to_disk = null
before_update = null
[training.batcher]
@batchers = "spacy.batch_by_words.v1"
discard_oversize = false
tolerance = 0.2
get_length = null
[training.batcher.size]
@schedules = "compounding.v1"
start = 100
stop = 1000
compound = 1.001
t = 0.0
[training.logger]
@loggers = "spacy.ConsoleLogger.v1"
progress_bar = false
[training.optimizer]
@optimizers = "Adam.v1"
beta1 = 0.9
beta2 = 0.999
L2_is_weight_decay = true
L2 = 0.01
grad_clip = 1.0
use_averages = false
eps = 0.00000001
learn_rate = 0.001
[training.score_weights]
ents_f = 1.0
ents_p = 0.0
ents_r = 0.0
ents_per_type = null
[pretraining]
[initialize]
vectors = ${paths.vectors}
init_tok2vec = ${paths.init_tok2vec}
vocab_data = null
lookups = null
before_init = null
after_init = null
[initialize.components]
[initialize.tokenizer]

18
project/backend/spacy-service/spacy_training/data_to_json.py
View File

@ -1,18 +0,0 @@
import os
import json
from training_data import TRAINING_DATA
# Setze hier den Pfad zu annotation_data.json
OUTFILE = os.path.join(os.path.dirname(__file__), "annotation_data.json")
json_list = []
for text, annot in TRAINING_DATA:
entities = []
for start, end, label in annot["entities"]:
entities.append([start, end, label])
json_list.append({"text": text, "entities": entities})
with open(OUTFILE, "w", encoding="utf8") as f:
json.dump(json_list, f, ensure_ascii=False, indent=2)
print("Alle Trainingsdaten wurden erfolgreich nach annotation_data.json migriert!")

3
project/backend/spacy-service/spacy_training/output/model-last/meta.json
View File

@ -19,16 +19,13 @@
"labels":{
"ner":[
"AUSSCH\u00dcTTUNGSRENDITE",
"IRR",
"KENNZAHL",
"LAUFZEIT",
"L\u00c4NDERALLOKATION",
"MANAGMENTGEB\u00dcHREN",
"RENDITE",
"RENDITE_X",
"RISIKOPROFIL",
"SEKTORENALLOKATION",
"TEST3243",
"ZIELAUSSCH\u00dcTTUNG",
"ZIELRENDITE"
]

BIN
project/backend/spacy-service/spacy_training/output/model-last/ner/model
View File

Binary file not shown.

2
project/backend/spacy-service/spacy_training/output/model-last/ner/moves
View File

@ -1 +1 @@
‚¥movesÚ,{"0":{},"1":{"KENNZAHL":-1,"RISIKOPROFIL":-2,"AUSSCH\u00dcTTUNGSRENDITE":-3,"LAUFZEIT":-4,"RENDITE":-5,"L\u00c4NDERALLOKATION":-6,"ZIELRENDITE":-7,"ZIELAUSSCH\u00dcTTUNG":-8,"MANAGMENTGEB\u00dcHREN":-9,"SEKTORENALLOKATION":-10,"TEST3243":-11,"IRR":-12,"RENDITE_X":-13},"2":{"KENNZAHL":-1,"RISIKOPROFIL":-2,"AUSSCH\u00dcTTUNGSRENDITE":-3,"LAUFZEIT":-4,"RENDITE":-5,"L\u00c4NDERALLOKATION":-6,"ZIELRENDITE":-7,"ZIELAUSSCH\u00dcTTUNG":-8,"MANAGMENTGEB\u00dcHREN":-9,"SEKTORENALLOKATION":-10,"TEST3243":-11,"IRR":-12,"RENDITE_X":-13},"3":{"KENNZAHL":-1,"RISIKOPROFIL":-2,"AUSSCH\u00dcTTUNGSRENDITE":-3,"LAUFZEIT":-4,"RENDITE":-5,"L\u00c4NDERALLOKATION":-6,"ZIELRENDITE":-7,"ZIELAUSSCH\u00dcTTUNG":-8,"MANAGMENTGEB\u00dcHREN":-9,"SEKTORENALLOKATION":-10,"TEST3243":-11,"IRR":-12,"RENDITE_X":-13},"4":{"":1,"KENNZAHL":-1,"RISIKOPROFIL":-2,"AUSSCH\u00dcTTUNGSRENDITE":-3,"LAUFZEIT":-4,"RENDITE":-5,"L\u00c4NDERALLOKATION":-6,"ZIELRENDITE":-7,"ZIELAUSSCH\u00dcTTUNG":-8,"MANAGMENTGEB\u00dcHREN":-9,"SEKTORENALLOKATION":-10,"TEST3243":-11,"IRR":-12,"RENDITE_X":-13},"5":{"":1}}£cfg<66>§neg_keyÀ
‚¥movesÚˆ{"0":{},"1":{"KENNZAHL":-1,"RISIKOPROFIL":-2,"AUSSCH\u00dcTTUNGSRENDITE":-3,"LAUFZEIT":-4,"RENDITE":-5,"L\u00c4NDERALLOKATION":-6,"ZIELRENDITE":-7,"ZIELAUSSCH\u00dcTTUNG":-8,"MANAGMENTGEB\u00dcHREN":-9,"SEKTORENALLOKATION":-10},"2":{"KENNZAHL":-1,"RISIKOPROFIL":-2,"AUSSCH\u00dcTTUNGSRENDITE":-3,"LAUFZEIT":-4,"RENDITE":-5,"L\u00c4NDERALLOKATION":-6,"ZIELRENDITE":-7,"ZIELAUSSCH\u00dcTTUNG":-8,"MANAGMENTGEB\u00dcHREN":-9,"SEKTORENALLOKATION":-10},"3":{"KENNZAHL":-1,"RISIKOPROFIL":-2,"AUSSCH\u00dcTTUNGSRENDITE":-3,"LAUFZEIT":-4,"RENDITE":-5,"L\u00c4NDERALLOKATION":-6,"ZIELRENDITE":-7,"ZIELAUSSCH\u00dcTTUNG":-8,"MANAGMENTGEB\u00dcHREN":-9,"SEKTORENALLOKATION":-10},"4":{"":1,"KENNZAHL":-1,"RISIKOPROFIL":-2,"AUSSCH\u00dcTTUNGSRENDITE":-3,"LAUFZEIT":-4,"RENDITE":-5,"L\u00c4NDERALLOKATION":-6,"ZIELRENDITE":-7,"ZIELAUSSCH\u00dcTTUNG":-8,"MANAGMENTGEB\u00dcHREN":-9,"SEKTORENALLOKATION":-10},"5":{"":1}}£cfg<66>§neg_keyÀ

24
project/backend/spacy-service/spacy_training/output/model-last/vocab/strings.json
View File

@ -273,11 +273,8 @@
"4,91",
"40",
"400",
"43",
"45",
"491",
"4r3",
"4t4r3",
"5",
"5%+",
"5,0",
@ -308,7 +305,6 @@
"67",
"7",
"7,1",
"7,2",
"7,5",
"7,5%+",
"7,50",
@ -1069,7 +1065,6 @@
"R.I.P.",
"RE",
"RENDITE",
"RENDITE_X",
"REV",
"REWE",
"RISIKOPROFIL",
@ -1084,10 +1079,8 @@
"Redaktion",
"Region",
"Regionen",
"Rendite",
"Rendite-",
"Rendite-Risiko-Profil",
"RenditeX",
"Renovierungen",
"Rents",
"Residential",
@ -1167,7 +1160,6 @@
"T",
"T.",
"TED",
"TEST3243",
"Tag",
"Target",
"Target-IRR",
@ -1308,7 +1300,6 @@
"Xxxxx-Xxxxx-Xxxxx",
"Xxxxx-xxx",
"Xxxxx-xxxx",
"XxxxxX",
"Xxxxx\u0308xx",
"Xxxxx\u0308xxx-Xxxxx",
"Xxxxx\u0308xxxx",
@ -1403,6 +1394,7 @@
"across",
"act",
"active",
"adasd23",
"add",
"adv",
"adv.",
@ -1559,11 +1551,9 @@
"berlin",
"bestandsentwicklung",
"bestandsentwicklungen",
"bestr\u00e4gt",
"betr",
"betr.",
"betreute",
"betr\u00e4gt",
"bev\u00f6lkerungsprognose",
"beziehungsweise",
"bez\u00fcglich",
@ -1694,6 +1684,7 @@
"d.h",
"d.h.",
"d.x",
"d23",
"dX",
"dXxx.\u20ac",
"d_d",
@ -1774,7 +1765,6 @@
"durchschnittlich",
"du\u2019s",
"dv.",
"dxdxd",
"dy",
"d\u00e4nemark",
"d\u2019",
@ -1939,7 +1929,6 @@
"festgelegt",
"festgelegter",
"ff",
"fhfhfh56",
"fierce",
"fil",
"financially",
@ -2040,7 +2029,6 @@
"ght",
"gic",
"gie",
"gjufzj45",
"gl.",
"global",
"globale",
@ -2062,7 +2050,6 @@
"h.",
"h.c",
"h.c.",
"h56",
"haltedauer",
"halten",
"halten-strategie",
@ -2262,7 +2249,6 @@
"ize",
"j",
"j.",
"j45",
"ja",
"jahr",
"jahre",
@ -2743,10 +2729,8 @@
"relationships",
"remains",
"ren",
"rendite",
"rendite-",
"rendite-risiko-profil",
"renditex",
"renegotiation",
"renovierungen",
"rent",
@ -2803,7 +2787,6 @@
"s.o",
"s.o.",
"s.w",
"s45",
"sa",
"sa.",
"sale",
@ -2814,7 +2797,6 @@
"scs",
"scsp",
"sd.",
"sdgds45",
"sector",
"sectors",
"sed",
@ -2920,7 +2902,6 @@
"tc.",
"td.",
"te-",
"teX",
"ted",
"tee",
"teflimmobilfe)-",
@ -3246,7 +3227,6 @@
"\u00e4",
"\u00e4.",
"\u00e4gl",
"\u00e4gt",
"\u00e4r.",
"\u00e4rzteh\u00e4user",
"\u00e4rzteh\u00e4usern",

9
project/backend/spacy-service/spacy_training/test.json
View File

@ -1,9 +0,0 @@
{
"id": "TEST",
"extracted_text_per_page": [
{
"page": 1,
"text": "Die Gesamtrendite beträgt 7,2 %."
}
]
}

563
project/backend/spacy-service/spacy_training/training_data.py
View File

@ -1,563 +0,0 @@
TRAINING_DATA = [
(
"Core",
{"entities": [[0, 4, "RISIKOPROFIL"]]},
),
(
"Core+",
{"entities": [[0, 5, "RISIKOPROFIL"]]},
),
(
"Core/Core+",
{"entities": [[0, 10, "RISIKOPROFIL"]]},
),
(
"Value Add",
{"entities": [[0, 9, "RISIKOPROFIL"]]},
),
(
"Core/Value Add",
{"entities": [[0, 14, "RISIKOPROFIL"]]},
),
(
"Core+/Value Add",
{"entities": [[0, 15, "RISIKOPROFIL"]]},
),
(
"Core/Core+/Value Add",
{"entities": [[0, 20, "RISIKOPROFIL"]]},
),
(
"The RE portfolio of the fund is a good illustration of Fond expertise in European core/core+ investments .",
{"entities": [[82, 92, "RISIKOPROFIL"]]},
),
(
"Risk level: Core/Core+",
{"entities": [[12, 22, "RISIKOPROFIL"]]},
),
(
"Different risk profile (core, core+, value-added)",
{"entities": [[24, 48, "RISIKOPROFIL"]]},
),
(
"Core/Core+ with OpCo premium",
{"entities": [[0, 10, "RISIKOPROFIL"]]},
),
(
"Core /Core+ Assets, well-established = Key Gateway Cities in Europe le.g. hotels in the market with minor asset London, Paris, Amsterdam, Berlin] management initiatives",
{"entities": [[0, 11, "RISIKOPROFIL"]]},
),
(
"Risikoprofil: Core, Core +",
{"entities": [[14, 26, "RISIKOPROFIL"]]},
),
(
"Name des Fonds Name des Investmentmanagers Allgemeine Informationen Name des Ansprechpartners Telefonnummer des Ansprechpartners E-Mail des Ansprechpartners Art des Anlagevehikels Struktur des Anlagevehikels Sitz des Anlagevehikels Struktur des Antagevehikels vom Manager festgelegter Stil Rechtsform Jahr des ersten Closings Laufzeit Geplantes Jahr der Auflösung Ziel-Netto-IRR / Gesamtrendite* Zielvolumen des Anlagevehikels Ziel-LTY ‚Aktueller LTV Ziirraiaein Maximaler LTV Zielregionfen)/Jand Zielsektoren Zielanlagestrategie INREV Fonds Offen Deutschland Core, Core + Offener Immobilien-Spezialfonds 2022 10 - 12 Jahre 2032 - 2034 7,50%+ 250 Mio. € 20% 0% 20% Führende Metropolregionen Deutschlands und ausgewählte Standorte >50T Einw. Wohnimmobilien Wertstabile Wohnimmobilien (mit Bestandsentwicklungen)",
{"entities": [[560, 572, "RISIKOPROFIL"]]},
),
(
"Core/Core+ strategy, with tactical exposure to development projects aiming at enhancing the quality of the portfolio over time",
{"entities": [[0, 10, "RISIKOPROFIL"]]},
),
(
"Strategie - Übersicht Risikoprofil Core+ Halten-Strategie Kaufen — Halten (langfristig) — Exit 1. Nachvermietungsstrategie Anlagestrategien 2. Standortaufwertungsstrategie 3. Strategie der Aufwertung der Immobilien Niederlande (max. 35 %) Länderallokation Frankreich (max. 35 %) (in % vom Zielvolumen) Skandinavien (Schweden, Dänemark) (max. 35 %) Deutschland (<= 10 %)",
{"entities": [[35, 40, "RISIKOPROFIL"]]},
),
(
"Core and Core+",
{"entities": [[0, 14, "RISIKOPROFIL"]]},
),
(
"core, core+, value-added",
{"entities": [[0, 24, "RISIKOPROFIL"]]},
),
(
"Manage to Core: max 20%",
{"entities": [[10, 14, "RISIKOPROFIL"]]},
),
(
"Benefits of the core/ core+ segment",
{"entities": [[16, 27, "RISIKOPROFIL"]]},
),
(
"Drawbacks of the core/ core+ segment",
{"entities": [[17, 28, "RISIKOPROFIL"]]},
),
(
"Why a Core / Core + investment program?",
{"entities": [[6, 19, "RISIKOPROFIL"]]},
),
(
"Different risk profile (core, core+, value-added)",
{"entities": [[24, 48, "RISIKOPROFIL"]]},
),
(
"INK MGallery Hotel Area: Amsterdam Core Tenant: Closed in 2018",
{"entities": [[35, 39, "RISIKOPROFIL"]]},
),
(
"A strategy targeting high quality Core and Core+ buildings, with defined SRI objectives, in order to extract value through an active asset management.",
{"entities": [[34, 48, "RISIKOPROFIL"]]},
),
(
"Navigate the diversity of the Core/Core+ investment opportunities in European Prime Cities",
{"entities": [[30, 40, "RISIKOPROFIL"]]},
),
(
"GEDis an open-ended Lux-based fund providing an attractive core/core+ real estate exposure, leveraging GRRE expertise in European RE markets. It offers diversification in terms of pan-European geographies and sectors: Offices, Retail and Hotels.",
{"entities": [[59, 69, "RISIKOPROFIL"]]},
),
(
"Core assets leave less room for active asset management value creation",
{"entities": [[0, 4, "RISIKOPROFIL"]]},
),
(
"capital preservation is defined here as a characteristic of core/core+ investments. There is no guarantee of capital.",
{"entities": [[60, 70, "RISIKOPROFIL"]]},
),
(
"Country / city BELGIUM Brussels BELGIUM Brussels SPAIN Madrid FRANCE Levallois FRANCE Paris 14 BELGIUM Brussels NETHERLANDS Rotterdam NETHERLANDS Rotterdam Sector Offices Offices Offices Offices Offices Offices Offices Logistics Risk Core",
{"entities": [[234, 238, "RISIKOPROFIL"]]},
),
(
"GERD(a balanced pan-European open ended retail fund — under the form of a French collective undertaking for Real Estate investments “OPCI”) is the flagship ofQin France and combines RE and listed assets (respective targets of 60% and 40%) with max. 40% leverage. The RE portfolio of the fund is a good illustration Of expertise in European core/core+ investments.",
{"entities": [[340, 350, "RISIKOPROFIL"]]},
),
(
"Prime office assets in Prime markets are very pricey unless rent reversion is real. Risk premium remains attractive on a leveraged basis. Manage to core or build to core can make sense as a LT investor in main cities. Residential is also attractive",
{"entities": [[148, 152, "RISIKOPROFIL"]]},
),
(
"Paris region is a deep and liquid market. Rents have some potential to improve. Considering current low yield and fierce competition, office right outside CBD for Core + assets can be considered. Manage to core strategies could make sense.",
{"entities": [[163, 169, "RISIKOPROFIL"]]},
),
(
"Lisbon is a small market but it experienced a rapid economic recovery in recent years and is interesting for Core Offices, quality Retail assetor Hotel walls with top operators. Limited liquidity of this market means investment must be small",
{"entities": [[109, 113, "RISIKOPROFIL"]]},
),
(
"4,0 %",
{"entities": [[0, 5, "AUSSCHÜTTUNGSRENDITE"]]},
),
(
"Prognostizierte jährliche Ausschüttung von 4,0%",
{"entities": [[44, 48, "AUSSCHÜTTUNGSRENDITE"]]},
),
(
"20% über einer @ Ausschüttungsrendite von 4,0%",
{"entities": [[44, 48, "AUSSCHÜTTUNGSRENDITE"]]},
),
(
"Prognostizierte Ausschüttungsrandite* Mindestanlage Mitgliedschaft Im Anlagesusschuss Ankaufs- / Verkaufs- / Verkaufs(Teflimmobilfe)- / Baumanagementgebahr (inkl. USt.) Parformanceabhängige Vergütung Einmalige Strukturierungsgebühr Laufzeit / Investtionszeltraum Ausschüttungsintervalle Deutsche Metropolregianen und umliegende Regionen mit Städten >50T Einwohner Artikel 8 Wohnimmobilien Deutschland ‚Aktive Bestandsentwicklung Offener Spezial-AlF mit festen Anlagebedingungen rd. 200 Mio. € / max. 20% rd. 250 Mio. € 7,5 % (nach Kosten & Gebühren, vor Steuern) 8 4,0 % {nach Kosten & Gebühren, var Steuern}",
{"entities": [[570, 575, "AUSSCHÜTTUNGSRENDITE"]]},
),
(
"5,00-5,25 % Ausschüttungsrendite",
{"entities": [[0, 11, "AUSSCHÜTTUNGSRENDITE"]]},
),
(
"Zielrendite 5,00-5,25 % Ausschüttungsrendite",
{"entities": [[12, 23, "AUSSCHÜTTUNGSRENDITE"]]},
),
(
"Auschüttungsrendite 4,9% 5,3%",
{"entities": [[21, 25, "AUSSCHÜTTUNGSRENDITE"]]},
),
(
"Auschüttungsrendite 4,9% 5,3%",
{"entities": [[26, 30, "AUSSCHÜTTUNGSRENDITE"]]},
),
(
"Auschittungsrendite 3,8% 5,7%",
{"entities": [[20, 24, "AUSSCHÜTTUNGSRENDITE"]]},
),
(
"Auschittungsrendite 3,8% 5,7%",
{"entities": [[25, 29, "AUSSCHÜTTUNGSRENDITE"]]},
),
(
"Auschüttungsrendite 4,5% 4,6%",
{"entities": [[21, 25, "AUSSCHÜTTUNGSRENDITE"]]},
),
(
"Auschüttungsrendite 4,5% 4,6%",
{"entities": [[26, 30, "AUSSCHÜTTUNGSRENDITE"]]},
),
(
"Auschüttungsrendite 5,0% 4,7%",
{"entities": [[26, 30, "AUSSCHÜTTUNGSRENDITE"]]},
),
(
"Auschüttungsrendite 5,0% 4,7%",
{"entities": [[21, 25, "AUSSCHÜTTUNGSRENDITE"]]},
),
(
"Auschüttungsrendite “eons a Nuremberg aha 5,0 % 4,8 %",
{"entities": [[43, 48, "AUSSCHÜTTUNGSRENDITE"]]},
),
(
"Auschüttungsrendite “eons a Nuremberg aha 5,0 % 4,8 %",
{"entities": [[49, 54, "AUSSCHÜTTUNGSRENDITE"]]},
),
(
"3-4% dividend yield",
{"entities": [[0, 4, "AUSSCHÜTTUNGSRENDITE"]]},
),
(
"Zielmärkte Klassifizierung SFDR Invastitionsfokus Rendite- / Risikoprofil Rechtsform Eigenkapital /FK Quote Investftionsvolumen Prognostizierte Gesamtrendite {IRR)* Prognostizierte Ausschüttungsrandite* Mindestanlage Mitgliedschaft Im Anlagesusschuss Ankaufs- / Verkaufs- / Verkaufs(Teflimmobilfe)- / Baumanagementgebahr (inkl. USt.) Parformanceabhängige Vergütung Einmalige Strukturierungsgebühr Deutsche Metropolregianen und umliegende Regionen mit Städten >50T Einwohner Artikel 8 Wohnimmobilien Deutschland ‚Aktive Bestandsentwicklung Offener Spezial-AlF mit festen Anlagebedingungen rd. 200 Mio. € / max. 20% rd. 250 Mio. € 7,5 % (nach Kosten & Gebühren, vor Steuern) 8 4,0 % {nach Kosten & Gebühren, var Steuern} 5Mio.€ Ab 10 Mio. € 1,40 % / 0,80 % /2,12% / 4,91 % Laufzeit / Investtionszeltraum Ausschüttungsintervalle 20 % über einer @ Ausschüttungsrendite von 4,0 % 0,1% der bis zum 31.12.2023 erfolgten Kapitalzusagen (max. 200.000 &) 10 bis 12 Jahre / bis zu 24 Monate angestrebt Mindestens jährlich",
{"entities": [[945, 960, "LAUFZEIT"]]},
),
(
"Laufzeit / Investtionszeltraum,10 bis 12 Jahre / bis zu 24 Monate angestrebt Ausschüttungsintervalle,Mindestens jährlich",
{"entities": [[31, 46, "LAUFZEIT"]]},
),
(
"10-12 Jahre Laufzeit bei einem LTV von bis zu 20%",
{"entities": [[0, 11, "LAUFZEIT"]]},
),
(
"vom Manager festgelegter Stil Rechtsform Jahr des ersten Closings Laufzeit Geplantes Jahr der Auflösung Ziel-Netto-IRR / Gesamtrendite* Zielvolumen des Anlagevehikels Ziel-LTY‚Aktueller LTV Zielsektoren Zielanlagestrategie Fonds Offen Deutschland Core, Core + Offener Immobilien-Spezialfonds 2022 10 - 12 Jahre",
{"entities": [[297, 310, "LAUFZEIT"], [247, 259, "RISIKOPROFIL"]]},
),
(
"Allgemeine Annahmen Ankaufsphase Haltedauer Zielobjektgröße Finanzierung Investitions-annahmen Zielrendite 24 Monate Investmentzeitraum 10 Jahre (+) EUR 20-75 Mio. Keine externe Finanzierung zum Auftakt (ausschließlich Darlehen der Anteilseigner). Die Finanzierung wird nach der Ankaufsphase und Stabilisierung der Zinssätze neu geprüft. Angestrebter LTV zwischen 25-40 % Investitionen für Renovierungen und ESG- Verbesserungen werden für jedes Objekt einzeln festgelegt. 5,00-5,25 % Ausschüttungsrendites",
{"entities": [[136, 148, "LAUFZEIT"], [472, 483, "AUSSCHÜTTUNGSRENDITE"]]},
),
(
"Zielrendite 5,00-5,25 % Ausschüttungsrendite 1) Ankauf von Objekten an Tag eins mit 100% Eigenkapital. Die Strategie unterstellt die Aufnahme von Fremdkapital, sobald sich die Zins- und Finanzierungskonditionen nachhaltig stabilisieren. Strategie - Übersicht Risikoprofil Core+",
{"entities": [[12, 23, "AUSSCHÜTTUNGSRENDITE"], [272, 277, "RISIKOPROFIL"]]},
),
(
"Vehicle lifetime / investment period Open-ended fund",
{"entities": [[37, 52, "LAUFZEIT"]]},
),
(
"Vehicle / domicile Alternative Investment Fund / Luxembourg (e.g. SCSp SICAV-RAIF) Investment strategy eturn pro Real Estate (PropCo + OpCo) Investing in upscale hotels with long-term management contracts in major European destinations Core/Core+ with OpCo premium Management Agreements solely with financially strong and experienced partners/ global brands Cash flow-oriented Cash-flow pattern Target equity /AuM € 400m equity / € 800m AuM (50% Loan-to-Value) Vehicle lifetime / investment period Open-ended fund",
{"entities": [[498, 513, "LAUFZEIT"], [236, 245, "RISIKOPROFIL"]]},
),
(
"Vehicle type (Lux-RAIF) (net of fees) IRR6.5% ACCOR Vehicle structure Open-ended Targetvehiclesize € 400m (equity) Manager-defined Core/Core+ with | style OpCo Premium darge CLV. 50% Pt H | LTO N WORLDWIDE Year of first closing 2020 Target no. ofinvestors 1-5 Fund life (yrs} Open-ended Min-commitmentper —¢ 400m",
{"entities": [[131, 141, "RISIKOPROFIL"], [70, 80, "LAUFZEIT"]]},
),
(
"Fund term: Open-ended",
{"entities": [[11, 21, "LAUFZEIT"]]},
),
(
"Abdeckung der Risiko-Rendite-Bandbreite (Core, Core+, Value-Add)",
{"entities": [[41, 63, "RISIKOPROFIL"]]},
),
(
"5,1% - 8,5% IRR!",
{"entities": [[0, 11, "RENDITE"]]},
),
(
"Retailinvestitionsvolumen nach Ländern (2024) Vereinigtes Königreich, 26,4% Deutschland, 19,0% Andere, 19,7% Italien, 8,2% Irland, 3,3% N | Frankreich, Spanien, 8,1%",
{"entities": [[46, 75, "LÄNDERALLOKATION"], [76, 94, "LÄNDERALLOKATION"], [95, 108, "LÄNDERALLOKATION"], [109, 122, "LÄNDERALLOKATION"], [123, 135, "LÄNDERALLOKATION"]]},
),
(
"Erwartete IRR 5 (je nach Objekt- ‘A(E) 6.00% - 8,00%",
{"entities": [[39, 52, "RENDITE"]]},
),
(
"Zielmarkts Deutsche Metropolregianen und umliegende Regionen mit Städten >50T Einwohner Klassifizierung SFDR Artikel 8 Invastitionsfokus Wohnimmobilien Deutschland Rendite- / Risikoprofil ‚Aktive Bestandsentwicklung Rechtsform Offener Spezial-AlF mit festen Anlagebedingungen Eigenkapital /FK Quote rd. 200 Mio. € / max. 20% Investftionsvolumen rd. 250 Mio. € Prognostiderte Gesamtrendite {IRR)* 7,5 % (nach Kosten & Gebühren, vor Steuern) Prognostizierte Ausschüttungsrandite* @ 4,0 % {nach Kosten & Gebühren, var Steuern} Mindestanlage 5Mio.€ Mitgliedschaft Im Anlagesusschuss Ab 10 Mio. € Ankaufs- / Verkaufs- / Verkaufs(Teflimmobilfe)- / Baumanagementgebahr (inkl. USt) 1,40 %/080%/212%/491% Parformanceabhängige Vergütung 20 % über einer ® Ausschüttungsrendite von 4,0% Einmalige Strukturierungsgebühr 0,1% der bis zum 31.12.2023 erfolgten Kapitalzusagen (max. 200.000 €) Laufzelt / Investtonszeltraum 10 bis 12 Jahre / bis zu 24 Monate angestrebt Ausschüttungsintervalle Mindestens jährlich",
{"entities": [[396, 401, "RENDITE"], [482, 487, "AUSSCHÜTTUNGSRENDITE"], [914, 929, "LAUFZEIT"]]},
),
(
"= Prognostizierte jährliche Ausschüttung von @ 4,0%* = Prognostizierte Gesamtrendite (IRR) von 7,5%*",
{"entities": [[48, 52, "AUSSCHÜTTUNGSRENDITE"], [96, 100, "RENDITE"]]},
),
(
"Prognose: 7,5%+ IRR auf Fondsebene",
{"entities": [[10, 14, "RENDITE"]]},
),
(
"= Prognostizierte jährliche Ausschüttung* von 84,0% = Prognostizierte Gesamtrendite (IRR}* von 7,5%",
{"entities": [[96, 100, "RENDITE"], [49, 53, "AUSSCHÜTTUNGSRENDITE"]]},
),
(
"= Lagefokussierung: Metropolregionen Deutschlands = Finanzierung: max. 20% LTV = Risikoprofil: Core, Core +",
{"entities": [[95, 107, "RISIKOPROFIL"]]},
),
(
"Performance-Fee: 20% über einer @ Ausschüttungsrendite von 4,0%",
{"entities": [[61, 65, "AUSSCHÜTTUNGSRENDITE"]]},
),
(
"Fondstyp Offener Spezial-AIF nach KAGB mit festen Anlagebedingungen ESG-Klassifizierung Fonds gemäß Artikel 8 EU-Offenlegungsverordnung KVG IntReal GmbH, Hamburg Anlagestrategie Aufbau eines Objektportfolios aus Ärztehäusern, die langfristig vermietet sind Ärztehäuser, Laborimmobilien, im Verbund mit Ärztehäusern auch ambulant Zielobjekte betreute Wohngemeinschaften; Mietanteil Medizin und medizinnahe Dienstleistungen/Handel > 65 % (Objektebene) WALT >5 Jahre bei Ankauf Objektbaujahre Ab 2000 Anlagegrenzen Einzelinvestment 8-30 Mio. EUR Anzahl Objekte 10-20 Deutschland bundesweit; jeweiliges Einzugsgebiet > 25.000 Einwohner mit Regionen stabiler Bevölkerungsprognose Risikoprofil Core / Core +",
{"entities": [[689, 702, "RISIKOPROFIL"]]},
),
(
"Fondsvolumen 300 Mio. EUR Zielrendite (IRR) > 6,0 % p. a. Ausschuttung >5,0 % p. a. Ankaufszeitraum 2024-2026 Laufzeit 31.12.2036 Mindestanlage 10 Mio. EUR Anlageausschuss Ja, entscheidet u. a. über Objekterwerb (Mitglied kann ab 20 Mio. EUR gestellt werden) Gebührenstruktur Marktüblich (auf Anfrage) Projektentwicklungen keine Forward-Deals Möglich, maximal 18 Monate Vorlauf; keine Projektentwicklungsrisiken beim Fonds Erbbaurechte Möglich, sofern Laufzeit > 60 Jahre und angemessene Entschädigung bei Ablauf und Heimfall Status Objektpipeline vorhanden: siehe Folie 16 ff.",
{"entities": [[44, 57, "RENDITE"], [71, 83, "AUSSCHÜTTUNGSRENDITE"], [120, 130, "LAUFZEIT"]]},
),
(
"Niederlande (max. 35 %) Länderallokation Frankreich (max. 35 %) (in % vom Zielvolumen) Skandinavien (Schweden, Dänemark) (max. 35 %) Deutschland (<= 10 %)",
{"entities": [[0, 23, "LÄNDERALLOKATION"], [41, 63, "LÄNDERALLOKATION"], [87, 132, "LÄNDERALLOKATION"], [133, 154, "LÄNDERALLOKATION"]]},
),
(
"Führender Immobilien-Investmentmanager in den Nordics für globale ll institutionelle Investoren in Value Add und Core Strategien",
{"entities": [[101, 119, "RISIKOPROFIL"]]},
),
(
"Core und Core+ Fonds",
{"entities": [[0, 14, "RISIKOPROFIL"]]},
),
(
"Risikoprofil Core / Core+",
{"entities": [[13, 25, "RISIKOPROFIL"]]},
),
(
"Durchschnittlich geplante jährliche Ausschüttung von 4,5-5,5% auf das investierte Eigenkapital an die Anleger Geplante Gesamtrendite von 5-6% (IRR) auf das eingezahlte Eigenkapital",
{"entities": [[54, 62, "AUSSCHÜTTUNGSRENDITE"], [138, 142, "RENDITE"]]},
),
(
"Geografische Zielallokation nach Investitionsphase des Fonds: 1) Schweden 20-60% Allokation Länder 2) Finnland 20-60% 3) Norwegen 10-40% 4) Dänemark 10-40%",
{"entities": [[65, 80, "LÄNDERALLOKATION"], [102, 117, "LÄNDERALLOKATION"], [121, 136, "LÄNDERALLOKATION"], [140, 155, "LÄNDERALLOKATION"]]},
),
(
"Deutsches Spezial-Sondervermögen mit festen Anlagebedingungen ($284 KAGB) Immobilien- oder Infrastrukturquote (nach Solvency II) Core / Core+ Euro Hauptstadtregionen und andere Großstädte in den Nordics €500 Mio. 4,5-5,5% 15 Jahre; Fonds hat unbegrenzte Laufzeit; Investmentphase 4 Jahre Maximaler Fremdkapitalanteil 50% (LTV-Ziel bei Ankauf), Langfristiges LTV-Ziel auf Fondsebene ist 45% 0,625% p. a. des Bruttofondsvermögens Zeichnungen ab € 30 Mio. - 0,03 % Rabatt Zeichnungen ab € 50 Mio. - zusatzl. 0,03 % Rabatt 1,1% des Verkehrswertes 0,6% der Bruttoverkaufswert 10% wenn Hurdle Rate 5,0 % p. a. (IRR netto) überschritten wird (nach 15 Jahren berechnet) Ja",
{"entities": [[129, 141, "RISIKOPROFIL"], [213, 221, "ZIELRENDITE"], [242, 262, "LAUFZEIT"]]},
),
(
"Standort Helsinki, Finnland Sektor Bildungswesen, Schule& Kindertagesstätte Vermietbare Fläche 3.321 m? Leerstand bei Ankauf 0% / 0% Ankaufspreis+ Investitionen €21,4 Mio. + €0,2 Mio Eigenkapital €21,6 Mio. Ankaufs- / Stabilisierungs- / Exitrendite 5,0%/ 5,5%/ 5,0% NOI zum Ankaufszeitpunkt / Exit-NOI €1.1m/ €1.2m Zielrenditen (netto für LPs) 5,4% IRR/ 1.5x EM / DY 4,3% Ankauf / Exit Dezember 2023/ Dezember 2033",
{"entities": [[345, 349, "ZIELRENDITE"]]},
),
(
"Evergreen/offene Fondsstrukturenv Core / Core+ Strategien",
{"entities": [[34, 46, "RISIKOPROFIL"]]},
),
(
"BEE Henderson German 2012 Logistik Core/D/Art. 8 € 336 Mio. 12 (voll investiert) 13,0 % p.a.",
{"entities": [[35, 39, "RISIKOPROFIL"], [81, 87, "RENDITE"]]},
),
(
"ICF German Logistics 2014 Logistik Core/D/Art. 8 € 400 Mio. 16 (voll investiert) 12,0 % p.a.",
{"entities": [[35, 39, "RISIKOPROFIL"], [81, 87, "RENDITE"]]},
),
(
"Individualmandat 2015 Logistik Core / D+AU/ ArTt. 6 € 200 Mio. 8 (realisiert) 8,0 % p.a.",
{"entities": [[31, 35, "RISIKOPROFIL"], [78, 83, "RENDITE"]]},
),
(
"European Logistics Partnership” 2017 Logistik Value-Add / Europ/a - € 1.000 Mio. 28 (realisiert) 20,0 % p.a.",
{"entities": [[46, 55, "RISIKOPROFIL"], [97, 103, "RENDITE"]]},
),
(
"European Core Logistics Fund (ECLF 1) 2021 Logistik Core / Euro/p Arat. 8 € 314 Mio. 12 (voll investiert) 7,50 % p.a.",
{"entities": [[9, 13, "RISIKOPROFIL"], [106, 112, "RENDITE"]]},
),
(
"P-Logistik Europa Fonds (ECLF 2) 2022 Logistik Core / Euro/p Arat. 8 € 150 Mio.? A (voll investiert) 6,5 % p.a.?",
{"entities": [[47, 51, "RISIKOPROFIL"], [101, 106, "RENDITE"]]},
),
(
"First Business Parks 2015 Light Industrial Value Add / D+AUT € 100 Mio. 6 (realisiert) 16,0 % p.a.",
{"entities": [[43, 52, "RISIKOPROFIL"], [87, 93, "RENDITE"]]},
),
(
"Unternehmensimmobilien Club 1 2016 Light Industrial Core+/D € 186 Mio. 9 (voll investiert) 13,0 % p.a.",
{"entities": [[91, 97, "RENDITE"]]},
),
(
"Unternehmensimmobilien Club 1 2016 Light Industrial Core+/D € 186 Mio. 9 (voll investiert) 13,0 % p.a.",
{"entities": [[52, 57, "RISIKOPROFIL"], [91, 97, "RENDITE"]]},
),
(
"Unternehmensimmobilien Club 2 2021 Light Industrial Core+/D € 262 Mio. 12 (voll investiert) 9,00 % p.a.",
{"entities": [[52, 57, "RISIKOPROFIL"], [92, 98, "RENDITE"]]},
),
(
"Individualmandat 2022 Light Industrial Value-Add / Nordics € 100 Mio. 5 (voll investiert) 18,0 % p.a.",
{"entities": [[39, 48, "RISIKOPROFIL"], [90, 96, "RENDITE"]]},
),
(
"EUROPEAN CORE LOGISTICS FUND 3",
{"entities": [[9, 13, "RISIKOPROFIL"]]},
),
(
"Core Investitionen",
{"entities": [[0, 4, "RISIKOPROFIL"]]},
),
(
"8 % IRR",
{"entities": [[0, 3, "RENDITE"]]},
),
(
"Rendite-Risiko-Profil Core ° Geographischer Fokus Kontinentaleuropaische Kernvolkswirtschaften nach Allokationsprofil * Sektoraler Fokus Logistikimmobilien nach Allokationsprofil Kapitalstruktur ° Eigenkapital € 250 Mio. ° Fremdkapital 50 % angestrebt, max. 60 % der Immobilienwerte (Objektebene) °e Mindestzeichnung € 10 Mio. Vehikelstruktur ° Rechtsform Immobilien-Spezial-AlF mit festen Anlagebedingungen nach 3 284 KAGB ° Klassifikation Artikel 8 Offenlegungsverordnung ¢ Anlagehorizont 10 Jahre mit Verlängerungsoption um 2 Jahre! ° Geplante Auflage 01 2025 Performanceziel? ° Ausschüttung 6,0 % p.a. (Durchschnitt 10 Jahre Haltedauer) ° Interner Zinsfuß (IRR) 8,0 % p.a. (10 Jahre Haltedauer, Target-IRR)",
{"entities": [[22, 26, "RISIKOPROFIL"], [596, 601, "AUSSCHÜTTUNGSRENDITE"], [667, 672, "RENDITE"]]},
),
(
"Core/Core+, mit Cash-Flow-Stabilität",
{"entities": [[0, 10, "RISIKOPROFIL"]]},
),
(
"Zielausschüttung: min. 5,10%",
{"entities": [[24, 29, "ZIELAUSSCHÜTTUNG"]]},
),
(
"Zielrendite (IRR): min. 5,50%",
{"entities": [[24, 29, "ZIELRENDITE"]]},
),
(
"Rewe & Lidl Maxhütte-Haidhof é ae: 6 s Bahnhof Ankermieter REWE & Lidl er WALT 20 und 17 Jahre Miete p.a. 1.127.916 € Kaufpreis 21,43 Mio. € Faktor 19,00 x LTV / Zins 80% / 4,0% Ausschüttung 5,7 % IRR 7,1%",
{"entities": [[193, 198, "AUSSCHÜTTUNGSRENDITE"], [203, 207, "ZIELRENDITE"]]},
),
(
"Real Estate Prime Europe Access the Core of European Prime Cities with a green SRI fund including a genuine low carbon commitment",
{"entities": [[36, 40, "RISIKOPROFIL"]]},
),
(
"(FR, UK, DE, BE, NL, LU, Nordics, Allocation SP, IT, CH)",
{"entities": [[1, 32, "LÄNDERALLOKATION"], [45, 55, "LÄNDERALLOKATION"]]},
),
(
"IRR: 6% - 7%",
{"entities": [[5, 12, "RENDITE"]]},
),
(
"Europe | Germany 67 Value Add",
{"entities": [[9, 16, "LÄNDERALLOKATION"], [20, 29, "RISIKOPROFIL"]]},
),
(
"Germany, Norway 336 Core Plus",
{"entities": [[0, 7, "LÄNDERALLOKATION"], [20, 29, "RISIKOPROFIL"]]},
),
(
"UK",
{"entities": [[0, 2, "LÄNDERALLOKATION"]]},
),
(
"NORWAY",
{"entities": [[0, 6, "LÄNDERALLOKATION"]]},
),
(
"9.8% IRR",
{"entities": [[0, 4, "RENDITE"]]},
),
(
"Investment volume down 52% to €2.3 billion, with 4,000 100 14% value-add and core-plus increasing YoY",
{"entities": [[63, 86, "RISIKOPROFIL"]]},
),
(
"Geared Gross IRR‘ seeking a range of 16-18% per annum",
{"entities": [[37, 43, "RENDITE"]]},
),
(
"Open-ended fund 24 months, incl. rolling reinvestment Sale of individual assets with respective management contracts or geared leases IRR: >6.5% | CoC: >5.0%",
{"entities": [[0, 10, "LAUFZEIT"], [139, 144, "RENDITE"]]},
),
(
"Our investment strategy focuses on investing in upscale hotels in European prime locations, including DACH, Italy, Spain, Portugal, France, UK, Denmark, Benelux,and Poland.",
{"entities": [[102, 171, "LÄNDERALLOKATION"]]},
),
(
"Core+ assets with value-add potential, Emerging Gateway Cities Helsinki] Core+ with Value well-mitigated risk and great upside Potential potential through asset improvement or = Max. 20% UK & Ireland {no contract renegotiation currency risk hedging], 80% tinental E > IRR target of 6-9%",
{"entities": [[0, 5, "RISIKOPROFIL"], [282, 286, "RENDITE"]]},
),
(
"10% net IRR since inception in 2018?",
{"entities": [[0, 3, "RENDITE"]]},
),
(
"Eurozone: Benelux, France and Germany",
{"entities": [[10, 37, "LÄNDERALLOKATION"]]},
),
(
"Open-ended, with quarterly liquidity (redemption rights, dual pricing)",
{"entities": [[0, 10, "LAUFZEIT"]]},
),
(
"Class A & B (Institutional): 0.93% on NAV; Class D (Wholesale): 1.80% on NAV; Class P (Wholesale): 1.25% on NAV",
{"entities": [[29, 34, "MANAGMENTGEBÜHREN"], [64, 69, "MANAGMENTGEBÜHREN"], [99, 104, "MANAGMENTGEBÜHREN"]]},
),
(
"Risk profile: favour core > © at least and core+ assets with a targeted N 2 n allocation to value add assets to enhance returns",
{"entities": [[21, 25, "RISIKOPROFIL"], [43, 48, "RISIKOPROFIL"]]},
),
(
"The Netherlands (38 assets) = Germany (9 assets) 10 largest Country assets split France (8 assets)",
{"entities": [[0, 15, "LÄNDERALLOKATION"], [30, 37, "LÄNDERALLOKATION"], [81, 87, "LÄNDERALLOKATION"]]},
),
(
"Expected IRR 10.9%",
{"entities": [[13, 18, "ZIELRENDITE"]]},
),
(
"Structure Open-end, perpetual life, Luxembourg domiciled Initial Target Size* €2 billion 6-8% total return,",
{"entities": [[10, 18, "LAUFZEIT"], [89, 93, "RENDITE"]]},
),
(
"Geographic Focus: UK, Ireland, Iberia, Nordics, Netherlands, Germany, France, Italy",
{"entities": [[18, 83, "LÄNDERALLOKATION"]]},
),
(
"IRR of 13-14%",
{"entities": [[7, 13, "RENDITE"]]},
),
(
"Value-add",
{"entities": [[0, 9, "RISIKOPROFIL"]]},
),
(
"Geographic allocation NORDICS UNITED KINGDOM GERMANY FRANCE PORTUGAL BENELUX",
{"entities": [[22, 76, "LÄNDERALLOKATION"]]},
),
(
"Strong track record delivering a 17% net IRR, 1.7x net multiple across all divested assets (both discretionary and non-discretionary mandates)",
{"entities": [[33, 36, "RENDITE"]]},
),
(
"Targeting a 7-8% net annual return and a 3-4% dividend yield, reflecting a target LTV of 35% (capped at 37.5%)",
{"entities": [[12, 16, "RENDITE"]]},
),
(
"Sweden Norway Denmark Finland",
{"entities": [[0, 29, "LÄNDERALLOKATION"]]},
),
(
"Logistics Residential Office Other",
{"entities": [[0, 34, "SEKTORENALLOKATION"]]},
),
(
"Fund Term Open-ended with an initial 24-month lock-in for new investors",
{"entities": [[10, 20, "LAUFZEIT"]]},
),
(
"Management fee of 85 bps on NAV.",
{"entities": [[18, 24, "MANAGMENTGEBÜHREN"]]},
),
(
"Core/Core+ strategy, with tactical exposure to development projects aiming at enhancing the quality of the portfolio over time",
{"entities": [[0, 10, "RISIKOPROFIL"]]},
),
(
"Fund term: Open-ended",
{"entities": [[11, 21, "LAUFZEIT"]]},
),
(
"Return targets: The fund targets a net internal rate of return (IRR) of 8% and a net annual income yield of 5% with planned quarterly distributions’.",
{"entities": [[72, 74, "RENDITE"]]},
),
(
"Geographic scope: The fund has a broad mandate to invest in commercial and residential real estate across Sweden, Denmark, Finland, and Norway. 50% LTV Asset selection: Heirs to acquire high-quality, income-generating properties in major Nordic cities and enhance their value through active asset management. Portfolio construction: The goal is to build diversified portfolios that are appealing to core buyers upon exit.",
{"entities": [[106, 142, "LÄNDERALLOKATION"]]},
),
(
"Experience: Since 2012, | | has demonstrated its capability to build diversified and resilient portfolios for its core-plus funds. German Real Estate Quota advantage . Local expertise: extensive local relationships and proprietary deal flow in key Nordic markets provide a strategic advantage.",
{"entities": [[114, 123, "RISIKOPROFIL"]]},
),
(
"Target returns: 8% net IRR with 5% net annual income yield! * Geographic focus: Sweden, Denmark, Norway and Finland « Target leverage: 50% LTV (excluding short-term borrowing) « Sector exposure: office, logistics, public properties, retail (focused on grocery anchored and necessity driven retail) and residentials « Investment focus: high quality properties,",
{"entities": [[16, 18, "RENDITE"], [80, 115, "LÄNDERALLOKATION"], [195, 239, "SEKTORENALLOKATION"]]},
),
(
"The Fund 2 xemoours common limited partnership (SCS) (SICAV-RAIF) Investment Objective To pursue investments in commercial and residential properties throughout the Nordic Region Fund Target Size €300 million (equity) Return Targets Target net IRR of 8%, target net annual income yield of 5%",
{"entities": [[251, 253, "RENDITE"]]},
)
]

40
project/backend/spacy-service/spacy_training/training_model.py
View File

@ -1,40 +0,0 @@
import os
from pathlib import Path
import spacy
from spacy.cli.train import train
from spacy.tokens import DocBin
from tqdm import tqdm
from training_data import TRAINING_DATA
nlp = spacy.blank("de")
# create a DocBin object
db = DocBin()
for text, annot in tqdm(TRAINING_DATA):
doc = nlp.make_doc(text)
ents = []
# add character indexes
for start, end, label in annot["entities"]:
span = doc.char_span(start, end, label=label, alignment_mode="contract")
if span is None:
print(f"Skipping entity: |{text[start:end]}| Start: {start}, End: {end}, Label: {label}")
else:
ents.append(span)
# label the text with the ents
doc.ents = ents
db.add(doc)
# save the DocBin object
os.makedirs("./data", exist_ok=True)
db.to_disk("./data/train.spacy")
config_path = Path("config.cfg")
output_path = Path("output")
print("Starte Training...")
train(config_path, output_path)

2
project/docker-compose.yml
View File

@ -37,8 +37,6 @@ services:
retries: 10
ports:
- 5050:5000
volumes:
- ./backend/spacy-service/spacy_training:/app/spacy_training
ocr:
build:

1
project/frontend/package.json
View File

@ -23,7 +23,6 @@
"@tanstack/react-router": "^1.114.3",
"@tanstack/react-router-devtools": "^1.114.3",
"@tanstack/router-plugin": "^1.114.3",
"file-saver": "^2.0.5",
"react": "^19.0.0",
"react-dom": "^19.0.0",
"react-material-file-upload": "^0.0.4",

7
project/frontend/src/components/KPIForm.tsx
View File

@ -400,13 +400,14 @@ export function KPIForm({ mode, initialData, onSave, onCancel, loading = false,
function generateSpacyEntries(formData: Partial<Kennzahl>) {
const label = formData.name?.trim().toUpperCase() || "";
return (formData.examples || []).map(({ sentence, value }) => {
const start = sentence.indexOf(value);
const trimmedValue = value.trim();
const start = sentence.indexOf(trimmedValue);
if (start === -1) {
throw new Error(`"${value}" nicht gefunden in Satz: "${sentence}"`);
throw new Error(`"${trimmedValue}" nicht gefunden in Satz: "${sentence}"`);
}
return {
text: sentence,
entities: [[start, start + value.length, label]]
entities: [[start, start + trimmedValue.length, label]]
};
});
}

25
project/frontend/src/routes/config.tsx
View File

@ -78,23 +78,30 @@ function ConfigPage() {
}
};
const handleTriggerTraining = () => {
const handleTriggerTraining = async () => {
setTrainingRunning(true);
try {
const response = await fetch(`${API_HOST}/api/spacy/train`, {
method: "POST",
});
if (!response.ok) throw new Error("Training konnte nicht gestartet werden");
// Erfolgsmeldung erst hier anzeigen
setSnackbarMessage("Training wurde gestartet.");
setSnackbarOpen(true);
fetch(`${API_HOST}/api/spacy/train`, {
method: "POST",
}).catch(err => {
pollTrainingStatus(); // jetzt starten
} catch (err) {
console.error(err);
setSnackbarMessage("Fehler beim Starten des Trainings.");
setSnackbarOpen(true);
console.error(err);
});
pollTrainingStatus(); // Starte Überwachung
setTrainingRunning(false);
}
};
const pollTrainingStatus = () => {
const interval = setInterval(async () => {
try {