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The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
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_from_streaming.py", line 132, in compute_first_rows_response
iterable_dataset = iterable_dataset._resolve_features()
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2211, 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 1235, 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 1384, in __iter__
for key, example in ex_iterable:
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1040, 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 156, 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.Need help to make the dataset viewer work? Open a discussion for direct support.
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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