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Populate float_answer for Tapas Weak supervision for aggregation (WTQ). TypeError: Parameter to CopyFrom() must be instance of same class: expected language.tapas.Question got str.
#162
float_answer: the float answer to the question, if there is one (np.nan if there isn’t). Only required in case of weak supervision for aggregation (such as WTQ and WikiSQL)
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ayazhankadessova
changed the title
Hi Niels!
Populate float_answer for Tapas Weak supervision for aggregation (WTQ). TypeError: Parameter to CopyFrom() must be instance of same class: expected language.tapas.Question got str.
Jun 15, 2022
HI @ayazhankadessova
How you handled float_answer column ( in the case of string answers like name,place) if there is no answer .
and also how you handled answer_coordinates column ( in the case of aggregations answers like sum,avg etc)
if there is no answer
I am trying to fine-tune Tapas following the instructions here: https://huggingface.co/transformers/v4.3.0/model_doc/tapas.html#usage-fine-tuning , Weak supervision for aggregation (WTQ) using the https://www.microsoft.com/en-us/download/details.aspx?id=54253 , which follow the required format of dataset in the SQA format, tsv files with most of the named columns. But, there is no float_answer column. And as mentioned,
Since I am using WTQ, I need the float_answer column. I tried populating float_answer based on answer_text as suggested here, using https://github.com/google-research/tapas/blob/master/tapas/utils/interaction_utils_parser.py 's parse_question(table, question, mode) function. However, I am getting errors.
I copied everything from here and put these args:
.
But, I get this error: TypeError: Parameter to CopyFrom() must be instance of same class: expected language.tapas.Question got str.
1) Can you, please help understand what args should I Use or how else can I populate float_answer?
I am using table_csv and the question, answer to which is in the table given:
2) Also we have tried to simply add float_answer column and make all the values np.nan. Crashed, too.
encoding["float_answer"]
=torch.tensor(float("nan"))
Is there tutorial for WTQ fine-tuning? Thanx!
The text was updated successfully, but these errors were encountered: