feat(language_metrics): add q squared

.gitignore

@@ -132,6 +132,9 @@ ENV/
 env.bak/
 venv.bak/
 
+#sync
+sync.sh
+
 # Spyder project settings
 .spyderproject
 .spyproject

.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.

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"

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

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