Datasets:

GIZ
/

policy_qa_v0_1

Error code:   FeaturesError
Exception:    ValueError
Message:      Not able to read records in the JSON file at hf://datasets/GIZ/policy_qa_v0_1@9e44804580c6b926e60f0ef0c17dd9205131cd37/QA.json.
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 240, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2216, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1239, in _head
                  return _examples_to_batch(list(self.take(n)))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1389, in __iter__
                  for key, example in ex_iterable:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1044, in __iter__
                  yield from islice(self.ex_iterable, self.n)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 282, in __iter__
                  for key, pa_table in self.generate_tables_fn(**self.kwargs):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 165, in _generate_tables
                  raise ValueError(f"Not able to read records in the JSON file at {file}.") from None
              ValueError: Not able to read records in the JSON file at hf://datasets/GIZ/policy_qa_v0_1@9e44804580c6b926e60f0ef0c17dd9205131cd37/QA.json.

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This dataset is curated by GIZ Data Service Center. The source dataset for this comes from Internal GIZ team (IKI_Tracs) and Climatewatchdata, where Climatewatch has analysed Intended nationally determined contribution (INDC), NDC and Revised/Updated NDC of the countries to answer some important questions related to Climate change.

Specifications

Dataset size: ~85k
Language: English, French, Spanish

Columns

index (type:int): Unique Response ID
ResponseText (type:str): Annotated answer/response to query
Alpha3 (type:str):country alpha-3 code (ISO 3166)
Country (type:str): country name
Document (type:str):Name of type of Policy document from which response is provided
IkiInfo (type: list[dict]): Responsetext can appear/occur as answer/response for different kind of query, therefore in that case we preserve all raw information for each occurences. Each dictionary object represents one such occurrence for response and provides all raw metadata for an occurrence.In case of None, it means the entry belongs to Climate data and not IKI Tracs data)
CWInfo (type: list[dict]):Responsetext can appear/occur as answer/response for different kind of query, therefore in that case we preserve all raw information for each occurences. Each dictionary object represents one such occurrence for response and provides all raw metadata for an occurrence. In case of None, it means the entry belongs to Iki tracs data and not CW)
Source (type:list[str]): Contains the name of source
Target (type:list): Value at index 0, represents number of times ResponseText appears as 'Target', and not-Target (value at index 1 )
Action (type:list): Value at index 0, represents number of times ResponseText appears as 'Action', and not-Action (value at index 1 )
Policies_Plans (type:list): Value at index 0, represents number of times ResponseText appears as 'Policy/Plan', and not-Policy/Plan (value at index 1 )
Mitigation (type:list): Value at index 0, represents number of times ResponseText appears in reference to Mititgation and not-Mitigation (value at index 1 )
Adaptation (type:list): Value at index 0, represents number of times ResponseText appears in reference to Adaptation and not-Adaptation (value at index 1 )
language (type:str): ISO code of language of ResponseText.
context (type:list[str]): List of paragraphs/textchunk from the document of country which contains the ResponseText. These results are based on Okapi bm25 retriever, and hence dont represent ground truth.
context_lang (type:str): ISO code of language of ResponseText. In some cases context and ResponseText are different as annotator have provided the translated response, rather than original text from document.
matching_words(type:list[list[[words]]):For each context, finds the matching words from ResponseText (stopwords not considered).
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 (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)

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