Add long description for properties, and update some of the short one…

…s. (#2629)

deepchecks/nlp/utils/text_properties.py

@@ -714,18 +714,25 @@ class TextProperty(TypedDict):
     'Max Word Length': 'Maximum number of characters in a word',
     '% Special Characters': 'Percentage of special characters in the text',
     'Language': 'Language of the text, using the fasttext language detection model',
-    'Sentiment': 'Sentiment of the text, calculated using the TextBlob sentiment analysis model',
-    'Subjectivity': 'Subjectivity of the text, calculated using the TextBlob sentiment analysis model',
+    'Sentiment': 'Sentiment of the text, calculated using the TextBlob sentiment analysis model.'
+                 ' Ranging from -1 (negative) to 1 (positive)',
+    'Subjectivity': 'Subjectivity of the text, calculated using the TextBlob sentiment analysis model. Ranging from 0 '
+                    '(objective) to 1 (subjective)',
     'Average Words Per Sentence': 'Average number of words per sentence in the text',
-    'Readability Score': 'A score calculated based on Flesch reading-ease per text sample',
+    'Readability Score': 'How easy to read a text sample is, from 0 (hard to read) to 100 (easy).'
+                         ' Based on Flesch reading-ease score',
     'Lexical Density': 'Percentage of unique words in the text',
-    'Toxicity': 'Toxicity score using unitary/toxic-bert HuggingFace model',
-    'Fluency': 'Fluency score using prithivida/parrot_fluency_model HuggingFace model',
-    'Formality': 'Formality score using s-nlp/roberta-base-formality-ranker HuggingFace model',
+    'Toxicity': 'A measure of how harmful or offensive a text sample is (0 to 1), uses the Detoxify library '
+                'unitary/toxic-bert model',
+    'Fluency': 'A measure of the fluency of the text (0 to 1), using the prithivida/parrot_fluency_model'
+               ' model from the authors of the Parrot Paraphraser library',
+    'Formality': 'The formality / register of the text (0 to 1), using the s-nlp/roberta-base-formality-ranker'
+                 ' model by the Skolkovo Institute of Science and Technology',
     'Unique Noun Count': 'Number of unique noun words in the text',
     'URLs Count': 'Number of URLS per text sample',
     'Email Addresses Count': 'Number of email addresses per text sample',
     'Unique URLs Count': 'Number of unique URLS per text sample',
+    'Is English': 'Whether the text is in English (1) or not (0)',
     'Unique Email Addresses Count': 'Number of unique email addresses per text sample',
     'Unique Syllables Count': 'Number of unique syllables per text sample',
     'Reading Time': 'Time taken in seconds to read a text sample',

docs/source/nlp/usage_guides/nlp_properties_extended.rst

@@ -0,0 +1,88 @@
+.. _nlp__properties_ext:
+
+==========
+Properties
+==========
+
+Properties are one-dimensional values that are calculated on each text sample. For example, a property could be simple
+text characteristics such as the number of words in the text, or more complex properties such identifying if the
+text contains toxic language.
+
+Link Validly
+------------
+
+The Link Validity property represents the ratio of number links in the text that are valid links, divided by the total
+number of links. A valid link is a link that returns a **200 OK** HTML status when sent a HTTP HEAD request. For text
+without links, the property will always return 1 (all links valid).
+
+Readability Score
+-----------------
+
+A score calculated based on the
+`Flesch reading-ease <https://en.wikipedia.org/wiki/Flesch%E2%80%93Kincaid_readability_tests#Flesch_reading_ease>`__,
+calculated for each text sample. The score typically ranges from 0
+(very hard to read, requires intense concentration) to 100 (very easy to read) for english text, though in theory the
+score can range from -inf to 206.835 for arbitrary strings.
+
+Toxicity
+--------
+
+The Toxicity property is a measure of how harmful or offensive a text is. The Toxicity property uses a pre-trained model
+called `unitary/toxic-bert <https://huggingface.co/unitary/toxic-bert>`__ on HuggingFace, which is created and
+maintained by the `Detoxify <https://github.com/unitaryai/detoxify>`__ team, based on the BERT
+architecture and trained on a large corpus of toxic comments. The model assigns a toxicity score to each text,
+ranging from 0 (not toxic) to 1 (very toxic).
+
+Fluency
+-------
+
+The Fluency property is a score between 0 and 1 representing how “well” the input text is written, or how close it is
+to being a sample of fluent English text. A value of 0 represents very poorly written text, while 1 represents perfectly
+written English. The property uses a pre-trained model called
+`prithivida/parrot_fluency_model <https://huggingface.co/prithivida/parrot_fluency_model>`__ on HuggingFace, which
+was created by the authors of the `Parrot Paraphraser <https://github.com/PrithivirajDamodaran/Parrot_Paraphraser>`__
+library.
+
+Formality
+---------
+
+The Formality model returns a measure of how formal the input text is. It uses a pre-traind model called
+`s-nlp/roberta-base-formality-ranker <https://huggingface.co/s-nlp/roberta-base-formality-ranker>`__ on HuggingFace,
+trained by the Skolkovo Institute of Science and Technology (s-nlp).
+The model was trained to predict for English sentences, whether they are formal or informal, where a score of 0
+represents very informal text, and a score of 1 very formal text.
+The model uses the roberta-base architecture, and was trained on
+`GYAFC <https://github.com/raosudha89/GYAFC-corpus>`__ from
+`Rao and Tetreault, 2018 <https://aclanthology.org/N18-1012>`__ and online formality corpus from
+`Pavlick and Tetreault, 2016 <https://aclanthology.org/Q16-1005>`__.
+
+Avoided Answer
+--------------
+
+The Avoided Answer property calculates the probability (0 to 1) of how likely it is that the LLM avoided answering a
+question.
+The property uses a pre-trained bert architecture model that was trained on a dataset of questions and LLM answers
+collected from various LLMs, where the model was trained to predict whether the answer is an avoidance or not.
+
+Grounded in Context
+-------------------
+
+The Grounded in Context Score is a measure of how well the LLM output is grounded in the context of the conversation,
+ranging from 0 (not grounded) to 1 (fully grounded).
+In the definition of this property, grounding means that the LLM output is based on the context given to it as part of
+the input, and not on external knowledge, for example knowledge that was present in the LLM training data. Grounding
+can be thought of as kind of hallucination, as hallucination are outputs that are not grounded in the context, nor
+are true to the real world.
+
+The property is especially useful for evaluating use-cases such as Question Answering, where the LLM is expected to
+answer questions based on the context given to it as part of the input, and not based on external knowledge. An example
+for such a use-case would be Question Answering based on internal company knowledge, where introduction of external
+knowledge (that, for example, may be stale) into the answers is not desired - we can imagine a case in which an LLM is
+asked a question about the company's revenue, and the answer is based on the company's internal financial reports, and
+not on external knowledge such as the company's Wikipedia page. In the context of Question Answering, any answer that
+is not grounded in the context can be considered a hallucination.
+
+The property is calculated by identifying key entities and quantities in the LLM output, such as names, places, dates
+and prices, and then identifying the same entities and quantities in the input given to the LLM.
+The property is calculated as the ratio of the number of entities/quantities in the LLM output that are also in the
+input, divided by the total number of entities/quantities in the LLM output.