diff --git a/.gitignore b/.gitignore
index 7d33cdb..990c88d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -132,6 +132,9 @@ ENV/
 env.bak/
 venv.bak/
 
+#sync
+sync.sh
+
 # Spyder project settings
 .spyderproject
 .spyproject
diff --git a/.pylintrc b/.pylintrc
index 7df4d41..853c5ec 100644
--- a/.pylintrc
+++ b/.pylintrc
@@ -188,7 +188,8 @@ contextmanager-decorators=contextlib.contextmanager
 # expressions are accepted.
 generated-members=REQUEST,
                   acl_users,
-                  aq_parent
+                  aq_parent,
+                  torch.argmax
 
 # Tells whether missing members accessed in mixin class should be ignored. A
 # class is considered mixin if its name matches the mixin-class-rgx option.
diff --git a/poetry.lock b/poetry.lock
index 9415fb4..ef40d28 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -375,101 +375,101 @@ files = [
 
 [[package]]
 name = "charset-normalizer"
-version = "3.3.0"
+version = "3.3.1"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
 optional = false
 python-versions = ">=3.7.0"
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 ]
 
 [[package]]
@@ -802,6 +802,23 @@ files = [
 [package.extras]
 dev = ["coverage", "coveralls", "pytest"]
 
+[[package]]
+name = "en-core-web-sm"
+version = "3.7.0"
+description = "English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler, lemmatizer."
+optional = false
+python-versions = "*"
+files = [
+    {file = "en_core_web_sm-3.7.0-py3-none-any.whl", hash = "sha256:6215d71a3212690e9aec49408a27e3fe6ad7cd6c715476e93d70dc784041e93e"},
+]
+
+[package.dependencies]
+spacy = ">=3.7.0,<3.8.0"
+
+[package.source]
+type = "url"
+url = "https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.7.0/en_core_web_sm-3.7.0-py3-none-any.whl"
+
 [[package]]
 name = "evaluate"
 version = "0.4.1"
@@ -978,6 +995,23 @@ ufo = ["fs (>=2.2.0,<3)"]
 unicode = ["unicodedata2 (>=15.0.0)"]
 woff = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "zopfli (>=0.1.4)"]
 
+[[package]]
+name = "fr-core-news-sm"
+version = "3.7.0"
+description = "French pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute_ruler, lemmatizer."
+optional = false
+python-versions = "*"
+files = [
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+]
+
+[package.dependencies]
+spacy = ">=3.7.0,<3.8.0"
+
+[package.source]
+type = "url"
+url = "https://github.com/explosion/spacy-models/releases/download/fr_core_news_sm-3.7.0/fr_core_news_sm-3.7.0-py3-none-any.whl"
+
 [[package]]
 name = "frozenlist"
 version = "1.4.0"
@@ -1050,13 +1084,13 @@ files = [
 
 [[package]]
 name = "fsspec"
-version = "2023.9.2"
+version = "2023.10.0"
 description = "File-system specification"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -1259,18 +1293,18 @@ numpy = ">=1.17.3"
 
 [[package]]
 name = "huggingface-hub"
-version = "0.17.3"
+version = "0.18.0"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 optional = false
 python-versions = ">=3.8.0"
 files = [
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 ]
 
 [package.dependencies]
 filelock = "*"
-fsspec = "*"
+fsspec = ">=2023.5.0"
 packaging = ">=20.9"
 pyyaml = ">=5.1"
 requests = "*"
@@ -2005,21 +2039,21 @@ files = [
 
 [[package]]
 name = "networkx"
-version = "3.1"
+version = "3.2"
 description = "Python package for creating and manipulating graphs and networks"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
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 [package.extras]
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+default = ["matplotlib (>=3.5)", "numpy (>=1.22)", "pandas (>=1.4)", "scipy (>=1.9,!=1.11.0,!=1.11.1)"]
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+test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"]
 
 [[package]]
 name = "nltk"
@@ -3211,32 +3245,37 @@ torch = ["safetensors[numpy]", "torch (>=1.10)"]
 
 [[package]]
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 [package.dependencies]
@@ -3907,117 +3946,56 @@ files = [
 
 [[package]]
 name = "tokenizers"
-version = "0.14.1"
-description = ""
+version = "0.13.3"
+description = "Fast and Customizable Tokenizers"
 optional = false
-python-versions = ">=3.7"
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 files = [
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-[package.dependencies]
-huggingface_hub = ">=0.16.4,<0.18"
-
 [package.extras]
-dev = ["tokenizers[testing]"]
-docs = ["setuptools_rust", "sphinx", "sphinx_rtd_theme"]
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+docs = ["setuptools-rust", "sphinx", "sphinx-rtd-theme"]
 testing = ["black (==22.3)", "datasets", "numpy", "pytest", "requests"]
 
 [[package]]
@@ -4119,39 +4097,39 @@ telegram = ["requests"]
 
 [[package]]
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 description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
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 ]
 
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+dev = ["GitPython (<3.1.19)", "Pillow (<10.0.0)", "accelerate (>=0.20.3)", "av (==9.2.0)", "beautifulsoup4", "black (>=23.1,<24.0)", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "decord (==0.6.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flax (>=0.4.1,<=0.7.0)", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "ray[tune]", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (>=0.0.241,<=0.0.259)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorflow (>=2.6,<2.15)", "tensorflow-text (<2.15)", "tf2onnx", "timeout-decorator", "timm", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.10,!=1.12.0)", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
+dev-tensorflow = ["GitPython (<3.1.19)", "Pillow (<10.0.0)", "beautifulsoup4", "black (>=23.1,<24.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (>=0.0.241,<=0.0.259)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorflow (>=2.6,<2.15)", "tensorflow-text (<2.15)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "urllib3 (<2.0.0)"]
+dev-torch = ["GitPython (<3.1.19)", "Pillow (<10.0.0)", "accelerate (>=0.20.3)", "beautifulsoup4", "black (>=23.1,<24.0)", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "librosa", "nltk", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "ray[tune]", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (>=0.0.241,<=0.0.259)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "timeout-decorator", "timm", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.10,!=1.12.0)", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
+docs = ["Pillow (<10.0.0)", "accelerate (>=0.20.3)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "hf-doc-builder", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune]", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.6,<2.15)", "tensorflow-text (<2.15)", "tf2onnx", "timm", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.10,!=1.12.0)", "torchaudio", "torchvision"]
 docs-specific = ["hf-doc-builder"]
 fairscale = ["fairscale (>0.3)"]
 flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)"]
@@ -4178,11 +4156,11 @@ tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>=2.6,<2.15)",
 tf-cpu = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>=2.6,<2.15)", "tensorflow-text (<2.15)", "tf2onnx"]
 tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
 timm = ["timm"]
-tokenizers = ["tokenizers (>=0.14,<0.15)"]
+tokenizers = ["tokenizers (>=0.11.1,!=0.11.3,<0.14)"]
 torch = ["accelerate (>=0.20.3)", "torch (>=1.10,!=1.12.0)"]
 torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
 torch-vision = ["Pillow (<10.0.0)", "torchvision"]
-torchhub = ["filelock", "huggingface-hub (>=0.16.4,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.14,<0.15)", "torch (>=1.10,!=1.12.0)", "tqdm (>=4.27)"]
+torchhub = ["filelock", "huggingface-hub (>=0.15.1,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.10,!=1.12.0)", "tqdm (>=4.27)"]
 video = ["av (==9.2.0)", "decord (==0.6.0)"]
 vision = ["Pillow (<10.0.0)"]
 
@@ -4658,4 +4636,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<3.11"
-content-hash = "dab7a7b6060ba4a9e674f1d62d7ee3f5330e51fbd896c70d8a2dd10acd9195ca"
+content-hash = "1beee4a28836d0a25d4b2503f697a8d4fc0cebdf52f9f97079378240e29c0e1c"
diff --git a/pyproject.toml b/pyproject.toml
index b40a01e..cce297a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -13,11 +13,13 @@ evaluate = "^0.4.1"
 scikit-learn = "^1.3.1"
 mauve-text = "^0.3.0"
 bert-score = "^0.3.13"
-tensorflow = "^2.14.0"
 bleurt = {git = "https://github.com/google-research/bleurt.git"}
-tensorflow-macos = {version = "2.14.0", platform = "darwin"}
+tensorflow = {version = "^2.14.0", platform = "linux"}
+tensorflow-macos = {version = "^2.14.0", platform = "darwin"}
 elemeta = "1.0.7"
 torch = ">=2.0.0, !=2.0.1, !=2.1.0"
+en-core-web-sm = {url = "https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.7.0/en_core_web_sm-3.7.0-py3-none-any.whl"}
+fr-core-news-sm = {url = "https://github.com/explosion/spacy-models/releases/download/fr_core_news_sm-3.7.0/fr_core_news_sm-3.7.0-py3-none-any.whl"}
 
 [tool.poetry.dev-dependencies]
 pylint = "^2.13"
diff --git a/saga_llm_evaluation_ml/model/__init__.py b/saga_llm_evaluation_ml/model/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/saga_llm_evaluation_ml/model/helpers/embedding_metrics.py b/saga_llm_evaluation_ml/model/helpers/embedding_metrics.py
index 6af0c38..4554ddf 100644
--- a/saga_llm_evaluation_ml/model/helpers/embedding_metrics.py
+++ b/saga_llm_evaluation_ml/model/helpers/embedding_metrics.py
@@ -2,15 +2,26 @@
 
 
 class BERTScore:
-    def __init__(self, model_type="distilbert-base-uncased"):
+    def __init__(self, lan="en", model_type=None):
         """
-        BERTScore computes a similarity score for each token in the candidate sentence with each token in the reference sentence.
-        The final score is the average of the similarity scores of all tokens in the candidate sentence.
+        BERTScore computes a similarity score for each token in the candidate sentence with each
+        token in the reference sentence. The final score is the average of the similarity scores of
+        all tokens in the candidate sentence.
 
         Args:
-            model_type (str, optional): Model type to use. Defaults to "roberta-large".
+            lan (str, optional): language to use. Defaults to "en", It may also be "fr". Depending
+            on the language, a different model is used by default.
+            model_type (sr, optional): Model to use. Defaults to None. If None, a default model is
+            used depending on the language (see above).
         """
-        self.model_type = model_type
+        if lan == "fr":
+            self.model_type = (
+                "distilbert-base-multilingual-cased" if not model_type else model_type
+            )  # TODO; find uncased version
+        elif lan == "en":
+            self.model_type = (
+                "distilbert-base-uncased" if not model_type else model_type
+            )
         self.metric = load("bertscore")
 
     def compute(self, references, predictions, **kwargs):
@@ -20,7 +31,8 @@ def compute(self, references, predictions, **kwargs):
             predictions (list): List of candidate sentences.
 
         Returns:
-            list: List of scores for each candidate sentence. Contains a list of scores for precisions, recalls, and F1 scores.
+            list: List of scores for each candidate sentence. Contains a list of scores for
+            precisions, recalls, and F1 scores.
         """
         assert len(references) == len(
             predictions
@@ -39,8 +51,8 @@ def compute(self, references, predictions, **kwargs):
 class MAUVE:
     def __init__(self, featurize_model_name="gpt2"):
         """
-        MAUVE score computes the difference between the candidate sentence distribution and the reference sentence distribution.
-        The bigger the MAUVE score, the better.
+        MAUVE score computes the difference between the candidate sentence distribution and the
+        reference sentence distribution. The bigger the MAUVE score, the better.
         """
         self.metric = load("mauve")
         self.featurize_model_name = featurize_model_name
diff --git a/saga_llm_evaluation_ml/model/helpers/language_metrics.py b/saga_llm_evaluation_ml/model/helpers/language_metrics.py
index 490a3f2..a38dedb 100644
--- a/saga_llm_evaluation_ml/model/helpers/language_metrics.py
+++ b/saga_llm_evaluation_ml/model/helpers/language_metrics.py
@@ -1,10 +1,27 @@
+import spacy
+import torch
 from evaluate import load
+from transformers import (
+    AutoModelForQuestionAnswering,
+    AutoModelWithLMHead,
+    AutoTokenizer,
+)
 
+from saga_llm_evaluation_ml.model.helpers.embedding_metrics import BERTScore
+from saga_llm_evaluation_ml.model.helpers.utils import (
+    INVALID_QUESTION,
+    NO_ANS,
+    filter_questions,
+    non_personal,
+)
 
+
+# pylint:disable=too-many-locals
 class BLEURTScore:
     def __init__(self, checkpoint="BLEURT-tiny"):
         """
-        BLEURT is a learnt metric that uses BERT to compute a similarity score for each token in the candidate sentence with each token in the reference sentence.
+        BLEURT is a learnt metric that uses BERT to compute a similarity score for each token
+        in the candidate sentence with each token in the reference sentence.
 
         Args:
             checkpoint (str, optional): Checkpoint to use. Defaults to BLEURT-tiny if not specified.
@@ -30,3 +47,204 @@ def compute(self, references, predictions, **kwargs):
         return self.metric.compute(
             predictions=predictions, references=references, **kwargs
         )
+
+
+class QSquared:
+    def __init__(self, lan="en") -> None:
+        """
+        Q² is a reference-free metric that aims to evaluate the factual consistency of knowledge-grounded
+        dialogue systems. The approach is based on automatic question generation and question answering
+        Source: https://github.com/orhonovich/q-squared
+
+        Args:
+            lan (str, optional): Language to use. Defaults to "en", It may also be "fr".
+        """
+        self.qa_tokenizer = AutoTokenizer.from_pretrained(
+            "ktrapeznikov/albert-xlarge-v2-squad-v2"
+        )
+        self.qa_model = AutoModelForQuestionAnswering.from_pretrained(
+            "ktrapeznikov/albert-xlarge-v2-squad-v2"
+        )
+        self.qg_tokenizer = AutoTokenizer.from_pretrained(
+            "mrm8488/t5-base-finetuned-question-generation-ap"
+        )
+        self.qg_model = AutoModelWithLMHead.from_pretrained(
+            "mrm8488/t5-base-finetuned-question-generation-ap"
+        )
+        assert lan in ["fr", "en"], "Language must be either fr or en"
+        self.bert_score = BERTScore(lan=lan)
+
+        if lan == "fr":
+            self.nlp = spacy.load("fr_core_news_sm")
+        elif lan == "en":
+            self.nlp = spacy.load("en_core_web_sm")
+
+    def get_answer(
+        self, question: str, text: str
+    ):  # Code taken from https://huggingface.co/transformers/task_summary.html
+        """
+        Search for the answer in the text given the question.
+        Args:
+            question (str) : question to ask
+            text (str) : text to search in
+        Returns:
+            answer (str) : answer to the question
+        """
+        inputs = self.qa_tokenizer.encode_plus(
+            question, text, add_special_tokens=True, return_tensors="pt"
+        )
+        input_ids = inputs["input_ids"].tolist()[0]
+
+        answer_start_scores, answer_end_scores = self.qa_model(
+            **inputs, return_dict=False
+        )
+
+        answer_start = torch.argmax(
+            answer_start_scores
+        )  # Get the most likely beginning of answer with the argmax of the score
+        answer_end = (
+            torch.argmax(answer_end_scores) + 1
+        )  # Get the most likely end of answer with the argmax of the score
+
+        ans = self.qa_tokenizer.convert_tokens_to_string(
+            self.qa_tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end])
+        )
+        return ans
+
+    def get_answer_candidates(self, text: str):
+        """
+        Look for candidate aswers that could be answered by the text.
+        Args:
+            text (str) : text to search in
+        Returns:
+            candidates (str) : candidates answers
+        """
+        doc = self.nlp(text)
+        candidates = [ent.text for ent in list(doc.ents)]
+        noun_chunks = list(doc.noun_chunks)
+        for chunk in noun_chunks:
+            found = False
+            for cand in candidates:
+                if chunk.text.lower() == cand.lower():
+                    found = True
+            if not found:
+                candidates.append(chunk.text)
+        # candidates += [chunk.text for chunk in list(doc.noun_chunks) if chunk.text not in candidates]
+        candidates = [cand for cand in candidates if cand.lower() != "i"]
+        return candidates
+
+    def get_questions_beam(
+        self, answer, context, max_length=128, beam_size=5, num_return=5
+    ):
+        """
+        Get the n best questions for a given answer, given the context. "Beam" is the name of the
+        approach
+        Args:
+            answer (str) : answer to the question
+            context (str) : context to search in
+            max_length (int, optional) : max length of the generated question. Defaults to 128.
+            beam_size (int, optional) : beam size. Defaults to 5.
+            num_return (int, optional) : number of questions to return. Defaults to 5.
+        Returns:
+            all_questions (list) : n best questions
+        """
+        all_questions = []
+        input_text = f"answer: {answer}  context: {context} </s>"
+        features = self.qg_tokenizer([input_text], return_tensors="pt")
+
+        beam_outputs = self.qg_model.generate(
+            input_ids=features["input_ids"],
+            attention_mask=features["attention_mask"],
+            max_length=max_length,
+            num_beams=beam_size,
+            no_repeat_ngram_size=3,
+            num_return_sequences=num_return,
+            early_stopping=True,
+        )
+
+        for beam_output in beam_outputs:
+            all_questions.append(
+                self.qg_tokenizer.decode(beam_output, skip_special_tokens=True).replace(
+                    "question: ", "", 1
+                )
+            )
+
+        return all_questions
+
+    def single_question_score(self, question, answer, response, knowledge):
+        """
+        Given a candidate pair of question and answer (generated from the candidate text), get the
+        score of the aswer given by taking as a context the knowledge that the LLM was given.
+        The higher the F1-score, the more the model we are trying to evaluate is consistent
+        with the knowledge.
+        Args:
+            question (str) : cadidate question (generated from the candidate text)
+            answer (str) : candidate answer (generated from the candidate text)
+            response (str) : text generated by the LLM
+            knowledge (str) : knowledge given as a context to the LLM
+
+        Returns:
+            score, answer (tuple) : bert-score of the knowledge answer, knowledge answer
+        """
+
+        pred_ans = self.get_answer(question, response)
+
+        if (
+            filter_questions(answer, pred_ans) == "VALID"
+        ):  # check if the answer is valid
+            knowledge_ans = self.get_answer(question, knowledge)
+            if knowledge_ans != NO_ANS:
+                score = self.bert_score.compute(
+                    references=[answer], predictions=[knowledge_ans]
+                )
+                return score["f1"][0], knowledge_ans
+            return 0, NO_ANS
+        return INVALID_QUESTION, INVALID_QUESTION
+
+    def compute(self, response, knowledge, single=False, remove_personal=True):
+        """
+        Compute the Q² score for a given response and knowledge.
+        Args:
+            response (str) : text generated by the LLM
+            knowledge (str) : knowledge given as a context to the LLM
+            single (bool) : if True, only one question is generated for each candidate answer.
+                            Defaults to False.
+            remove_personal (bool) : if True, remove questions that contain personal pronouns.
+                                     Defaults to True.
+        Returns:
+            avg_f1 (float) : average F1-bert-score of the knowledge answers (Q² score)
+        """
+
+        f1_bert_score = 0
+        num_questions = 0
+
+        # valid_questions = []
+        # valid_cands = []
+        # knowledge_answers = []
+        # scores = []
+
+        candidates = self.get_answer_candidates(response)
+        for cand in candidates:
+            questions = self.get_questions_beam(cand, response)
+            for question in questions:
+                if not remove_personal or non_personal(question, self.nlp):
+                    question_score, _ = self.single_question_score(
+                        question, cand, response, knowledge
+                    )
+                    if question_score != INVALID_QUESTION:
+                        num_questions += 1
+                        f1_bert_score += question_score
+
+                        # valid_questions.append(question)
+                        # valid_cands.append(cand)
+                        # knowledge_answers.append(knowledge_ans)
+                        # scores.append(question_score)
+
+                        if single:
+                            break
+
+        if num_questions:
+            avg_f1 = f1_bert_score / num_questions
+        else:
+            avg_f1 = INVALID_QUESTION
+        return avg_f1  # , valid_questions, valid_cands, knowledge_answers, scores
diff --git a/saga_llm_evaluation_ml/model/helpers/utils.py b/saga_llm_evaluation_ml/model/helpers/utils.py
index 5fd4a9f..b64f3e6 100644
--- a/saga_llm_evaluation_ml/model/helpers/utils.py
+++ b/saga_llm_evaluation_ml/model/helpers/utils.py
@@ -1,11 +1,16 @@
 import json
-from elemeta.nlp.metafeature_extractors_runner import (
-    MetafeatureExtractorsRunner,
-)
+import re
+import string
+from collections import Counter
+
+from elemeta.nlp.extractors.high_level.regex_match_count import RegexMatchCount
 from elemeta.nlp.extractors.high_level.word_regex_matches_count import (
     WordRegexMatchesCount,
 )
-from elemeta.nlp.extractors.high_level.regex_match_count import RegexMatchCount
+from elemeta.nlp.metafeature_extractors_runner import MetafeatureExtractorsRunner
+
+NO_ANS = "[CLS]"
+INVALID_QUESTION = -1
 
 
 def load_json(path):
@@ -14,6 +19,86 @@ def load_json(path):
     return json.loads(o_file)
 
 
+def filter_questions(exp_ans, pred_ans):
+    """
+    check if the expected answer and the predicted answer are the same.
+    Args:
+        exp_ans (str) : expected answer
+        pred_ans (str) : predicted answer
+    Returns:
+        str : "VALID" if the answers are the same, "NO MATCH" otherwise
+    """
+    if pred_ans == NO_ANS:
+        return "NO MATCH"
+    if clean_text(exp_ans) != clean_text(pred_ans):
+        return "NO MATCH"
+    return "VALID"
+
+
+def clean_text(text):
+    """
+    clean a text by removing punctuation and (some) stopwords.
+    Args:
+        text (str) : text to clean
+    Returns:
+        str : cleaned text
+    """
+    # TODO: improve
+    # TODO: add support to french language
+    text = text.lower()
+    text = text.translate(str.maketrans("", "", string.punctuation))
+    text = re.sub(r"\b(a|an|the|in|our)\b", " ", text)
+    return re.sub(" +", " ", text).strip()
+
+
+def raw_f1_score(a_gold, a_pred):
+    """
+    compute the raw F1 score between two answers.
+    Args:
+        a_gold (str) : expected answer
+        a_pred (str) : predicted answer
+    Returns:
+        float : F1 score
+    """
+    if a_pred == "":
+        return 0
+    gold_toks = clean_text(a_gold).split()
+    pred_toks = clean_text(a_pred).split()
+    common = Counter(gold_toks) & Counter(pred_toks)
+    num_same = sum(common.values())
+    if num_same == 0:
+        return 0
+    precision = 1.0 * num_same / len(pred_toks)
+    recall = 1.0 * num_same / len(gold_toks)
+    f1_score = (2 * precision * recall) / (precision + recall)
+    return f1_score
+
+
+def non_personal(question, nlp):
+    """
+    check if a question contains personal pronouns.
+    Args:
+        question (str) : question to check
+        nlp (spacy.lang) : spacy language model
+    Returns:
+        bool : True if the question does not contain personal pronouns, False otherwise
+    """
+    question_tok = nlp(question)
+    for tok in question_tok:
+        if tok.dep_ == "nsubj":
+            if (
+                tok.text.lower() == "i" or tok.text.lower() == "you"
+            ):  # TODO: add support to french language
+                return False
+        elif tok.dep_ == "poss":
+            if (
+                tok.text.lower() == "my" or tok.text.lower() == "your"
+            ):  # TODO: add support to french language
+                return False
+    return True
+
+
+# pylint:disable=invalid-name
 class MetadataExtractor:
     def __init__(self):
         self.metadata_extractor = MetafeatureExtractorsRunner()
diff --git a/tests/test_embedding_metrics.py b/tests/test_embedding_metrics.py
index 963fe23..d55eb84 100644
--- a/tests/test_embedding_metrics.py
+++ b/tests/test_embedding_metrics.py
@@ -1,6 +1,6 @@
 import unittest
 
-from saga_llm_evaluation_ml.model.helpers.embedding_metrics import BERTScore, MAUVE
+from saga_llm_evaluation_ml.model.helpers.embedding_metrics import MAUVE, BERTScore
 
 
 class TestBERTScore(unittest.TestCase):
diff --git a/tests/test_helpers.py b/tests/test_helpers.py
index 9d3f244..a1c0aa4 100644
--- a/tests/test_helpers.py
+++ b/tests/test_helpers.py
@@ -50,3 +50,5 @@ def test_add_regex(self):
         metadata = extractor.compute(text)
 
         self.assertGreater(len(metadata), len_metadata)
+        self.assertEqual(metadata["word_regex_matches_count_cat"], 1)
+        self.assertEqual(metadata["regex_match_count_cat"], 1)
diff --git a/tests/test_language_metrics.py b/tests/test_language_metrics.py
index 62090a7..7c85f91 100644
--- a/tests/test_language_metrics.py
+++ b/tests/test_language_metrics.py
@@ -1,6 +1,6 @@
 import unittest
 
-from saga_llm_evaluation_ml.model.helpers.language_metrics import BLEURTScore
+from saga_llm_evaluation_ml.model.helpers.language_metrics import BLEURTScore, QSquared
 
 
 class TestBLEURTScore(unittest.TestCase):
@@ -26,3 +26,32 @@ def test_compute_improved_input(self):
         scores = bleurt.compute([reference], [prediction])
         better_scores = bleurt.compute([reference], [better_prediction])
         self.assertGreater(better_scores["scores"], scores["scores"])
+
+
+class TestQSquared(unittest.TestCase):
+    def test_compute(self):
+        """Tests that the QSquared class computes the correct scores. And that the scores are the same when the same inputs are given."""
+        knowledges = [
+            "The beautiful cat sat on the ugly mat.",
+            "The ugly dog sat on the beautiful mat.",
+        ]
+        predictions = ["The cat sat on the mat.", "The dog sat on the mat."]
+
+        q_squared = QSquared()
+
+        for know, pred in zip(knowledges, predictions):
+            scores = q_squared.compute(pred, know, single=True)
+            scores_2 = q_squared.compute(pred, know, single=True)
+            self.assertEqual(scores, scores_2)
+
+    def test_compute_improved_input(self):
+        """Tests that the QSquared improves for a better prediction."""
+        knowledge = "Chronic urethral obstruction because of urinary calculi, prostatic hyperophy, tumors, normal pregnancy, tumors, uterine prolapse or functional disorders cause hydronephrosis which by definition is used to describe dilatation of renal pelvis and calculus associated with progressive atrophy of the kidney due to obstruction to the outflow of urine Refer Robbins 7yh/9,1012,9/e. P950"
+        prediction = "The cat sat on the mat."
+        better_prediction = "Chronic urethral obstruction due to benign prismatic hyperplasia can lead to hyperophy"
+
+        q_squared = QSquared()
+
+        scores = q_squared.compute(prediction, knowledge, single=True)
+        better_scores = q_squared.compute(better_prediction, knowledge, single=True)
+        self.assertGreater(better_scores, scores)