diff --git a/fund-ner/README.md b/fund-ner/README.md
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+++ b/fund-ner/README.md
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+# fund-ner
+
+Cette instance propose un outil de détection de financeurs.
+
+## Configuration
+
+L'application à utiliser est XXX.
+
+## Utilisation
+
+- [v1/funderTagger/funderTagger](#v1)
+
+### v1
+
+Ce web-service renvoie la liste des financeurs présent dans un texte.
+Il prend en entrée du JSON avec deux champs, `id` et `value`, et renvoie un JSON avec le la liste des financeurs trouvés dans le champ `value`.
+Il est important de souligner que le programme détécte en fonction du contexte de la phrase, il ne s'agit pas de détéction via une base de financeurs existante.
+
+#### Exemple de v1/first-name/gender
+
+Entrée
+```bash
+$ cat <<EOF | curl -X POST --data-binary @- "?????????"
+[{"id": "1", "value": "This study was funded by the CNRS and INIST."}]
+EOF
+```
+Sortie
+```json
+[
+     {"id": 1, "value": ["CNRS","INIST"]}}
+]
+ ```
diff --git a/fund-ner/examples.http b/fund-ner/examples.http
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+# Ces exemples peuvent être exécutés directement dans VSCode, en utilisant l'extension REST Client (humao.rest-client)
+# Décommenter/commenter les lignes voulues pour tester localement
+# @baseUrl=http://localhost:31976
+#@baseUrl=http://fund-ner.services.istex.fr/
+@baseUrl=http://fund-ner.tdm-services.intra.inist.fr/
+
+## WS Affiliation - RNSR - v2
+POST {{baseUrl}}/v1/funderTagger/funderTagger?indent=true HTTP/1.1
+Content-Type: application/json
+
+[
+  {
+    "id": 1,
+    "value": "This study was funded by the CNRS and INIST."
+  }
+]
diff --git a/fund-ner/swagger.json b/fund-ner/swagger.json
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+++ b/fund-ner/swagger.json
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+{
+    "openapi": "3.0.0",
+    "info": {
+      "title": "fund-ner - Detection de financeurs",
+      "summary": "Detection de financeurs",
+      "version": "1.0.0",
+      "termsOfService": "https://services.istex.fr/",
+      "contact": {
+        "name": "Inist-CNRS",
+        "url": "https://www.inist.fr/nous-contacter/"
+      }
+    },
+    "servers": [
+      {
+        "x-comment": "Will be automatically completed by the ezs server."
+      },
+      {
+        "url": "http://vptdmservices.intra.inist.fr:49241/",
+        "description": "Latest version for production",
+        "x-profil": "Standard"
+      }
+    ],
+    "tags": [
+      {
+        "name": "fund-ner",
+        "description": "Detection de financeurs",
+        "externalDocs": {
+          "description": "Plus de documentation",
+          "url": "https://gitbucket.inist.fr/tdm/web-services/fund-ner/README.md"
+        }
+      }
+    ]
+  }
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diff --git a/fund-ner/v1/funderTagger/best-model.pt.dvc b/fund-ner/v1/funderTagger/best-model.pt.dvc
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+++ b/fund-ner/v1/funderTagger/best-model.pt.dvc
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+outs:
+- md5: fdcb80f28184d1fb839f0575a9cffd39
+  size: 419082833
+  hash: md5
+  path: best-model.pt
diff --git a/fund-ner/v1/funderTagger/funderTagger.ini b/fund-ner/v1/funderTagger/funderTagger.ini
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+++ b/fund-ner/v1/funderTagger/funderTagger.ini
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+# OpenAPI Documentation - JSON format (dot notation)
+mimeType = application/json
+post.description = Trouve des financeurs dans un texte
+post.responses.default.description = Renvoie un Json id/value, où value est une liste de financeurs
+post.responses.default.content.application/json.schema.$ref =  #/components/schemas/JSONStream
+post.summary = Recherche de financeurs
+post.requestBody.required = true
+post.requestBody.content.application/json.schema.$ref = #/components/schemas/JSONStream
+post.parameters.0.in = query
+post.parameters.0.name = indent
+post.parameters.0.schema.type = boolean
+post.parameters.0.description = Indent or not the JSON Result
+
+# Examples
+
+post.requestBody.content.application/json.example.0.id = 1
+post.requestBody.content.application/json.example.0.value = This study was funded by the CNRS and INIST.
+post.responses.default.content.application/json.example.0.id = 1
+post.responses.default.content.application/json.example.0.value.0 = CNRS
+post.responses.default.content.application/json.example.0.value.1 = INIST
+
+
+[use]
+plugin = @ezs/spawn
+plugin = @ezs/basics
+plugin = @ezs/storage
+plugin = @ezs/analytics
+[JSONParse]
+separator = *
+[expand]
+path = env('path', 'value')
+size = 100
+# in production mode, uncomment the following line
+# cache = boost
+[expand/exec]
+# command should be executable !
+command = ./v1/funderTagger/funderTagger.py
+
+#command = ./expand.py
+[dump]
+indent = env('indent', false)
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diff --git a/fund-ner/v1/funderTagger/funderTagger.py b/fund-ner/v1/funderTagger/funderTagger.py
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+++ b/fund-ner/v1/funderTagger/funderTagger.py
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+#!/opt/bitnami/python/bin/python3.7
+
+from flair.data import Sentence
+from flair.models import SequenceTagger
+from normalize import normalize
+import json
+import sys
+import logging 
+
+logging.getLogger("flair").handlers[0].stream = sys.stderr
+tagger = SequenceTagger.load('./v1/funderTagger/best-model.pt')
+for line in sys.stdin:
+    data = json.loads(line)
+    lSent = normalize([data["value"]])[0].split()
+    sentence = Sentence()
+    for token in lSent:
+        sentence.add_token(token)
+    tagger.predict(sentence)
+    data["value"] = str([entity.text for entity in sentence.get_spans('ner')])
+    sys.stdout.write(json.dumps(data))
+    sys.stdout.write('\n') 
diff --git a/fund-ner/v1/funderTagger/normalize.py b/fund-ner/v1/funderTagger/normalize.py
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+++ b/fund-ner/v1/funderTagger/normalize.py
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+def normalize(words):
+    L = []
+    for word in words:
+        save = word
+        finalFile = []
+        end = ['.',',',";",":","»",")","’"]
+        start = ["«","(",".","‘"]
+        middle = ["l'","d'","j'","L'","D'","J'","l’","d’","j’","L’","D’","J’"]
+        queue = []
+
+        File = word.split()
+        for Word in File:
+            word = Word
+            for execp in start:
+                if word.startswith(execp):
+                    finalFile.append(word[0])
+                    word = word[1:]
+            for execp in middle:
+                if word.startswith(execp):
+                    finalFile.append(word[:2])
+                    word = word[2:]
+            for execp in end:
+                if word.endswith(execp):
+                    queue.insert(0,word[-1])
+                    word = word[:-1]
+            
+            finalFile.append(word)
+            for i in queue:
+                finalFile.append(i) 
+            queue = []
+
+        if finalFile == ["a"]:
+            #print("word = ",save)
+            pass
+        
+        L.append(" ".join(finalFile))
+    return L
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