Update README.md

README.md

@@ -48,30 +48,13 @@ Specifications
 - **response_words(type:list[words])**:Tokens/Words from ResponseText (stopwords not considered)
 - **context_wordcount (type:list[int])**: Number of tokens/words in each context (remember context itself is list of multiple strings, and stopwords not considered)
 - **strategy (type:str)**: Can take either of *small,medium,large* value. Represents the length of paragraphs/textchunk considered for finding the right context for ResponseText
-- 
-        "match_onresponse": [
-            0.86,
-            0.86,
-            0.71
-        ],
-        "candidate": [
-The list of Sectors covered include: Agriculture', 'Coastal Zone', 'Cross-Cutting Area', 'Education', 'Energy', 'Environment', 'Water', 'Buildings', 'Economy-wide', 'Industries', 'Transport', 'Waste', 'Health', 'LULUCF/Forestry', 'Social Development', 'Disaster Risk Management (DRM)', 'Urban','Tourism'.
-Some of the important question categories pertaining to climate change(adapted from climatewatchdata) include
-- Sectoral Policies
-- Sectoral Unconditional Actions
-- Building on existing downstream actions
-- Sectoral plans
-- Sectoral targets
-- Action and priority
-- Adapt Now sector
-- Emission reduction potential
-- Capacity Building Needs for Sectoral Implementation
-- Sectoral Conditional Actions
-- Technology Transfer Needs for Sectoral Implementation
-- Conditional part of mitigation target
-- Capacity building needs
-- Technology needs
-- Unconditional part of mitigation target
-- Time frame
-- Emission reduction potential
-No answer category like 'Squad2' is not part of dataset but can be easily curated from existing examples.
+- **match_onresponse (type:list[float])**: Percentage of overlapping words between Response and context with respect to the length of ResponseText.
+- **candidate (type:list[list[int]])**: Candidate within context which corresponds (fuzzy matching/similarity) to ResponseText. Value at index(0,1) represents (start,end) of string within context
+- **fetched_text (type:list[str])**: Candidate within context which corresponds (fuzzy matching/similarity) to ResponseText.
+- **response_translated(type:str)**:Translated ResponseText
+- **context_translated(type:str)**: Translated Context 
+- **candidate_translated(type:str)**: Translated Candidate index values (check column 'candidate')
+- **fetched_text_translated(type:str)**: Translated Candidates (check column 'candidate')
+- **QA_data(type:dict)**: Metadata about ResponseText, highlighting nature of query to which ResponseText corresponds as 'answer/response'
+- **match_onanswer (type:list[float])**: Represents percentage match between Response and candidate text ( from statistics it is recommended to keep only values above 0.3% as
+- answer and consider the context for 'No answer' for SQUAD2 data format)