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added datasets and models for text generation evaluation #291

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ashish3586
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@tongshuangwu
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Thanks, this is great! Can you also run these evals and add the numbers to the leaderboard readme?

@mille-s
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mille-s commented Sep 28, 2021

@ashish3586 can you please provide a short description of your transformation?

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@Saad-Mahamood Saad-Mahamood left a comment

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Minor change required. Just add the DocStrings for the input and return parameters for each of the functions.

dataset = KeyValueDataset.from_huggingface(
hf_dataset, TaskType.TEXT_TO_TEXT_GENERATION, ["text", "summary"]
)

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Missing return statement for "billsum".

dataset = KeyValueDataset.from_huggingface(
hf_dataset, TaskType.TEXT_TO_TEXT_GENERATION, ["text", "summary"]
)

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Also, I would suggest adding the 'else' block and raising exceptions with the proper message.

"summarization", model=model_name, tokenizer=model_name
"summarization", model=model_name, tokenizer=model_name, device=0 if is_cuda else -1)
#percent = f"[{split.split('[')[-1]}" if "[" in split else ""
#if dataset_name == "wikihow": split = "all[:1%]" # f"all{percent}"
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I think we can remove this commented code.

#if dataset_name == "wikihow": split = "all[:1%]" # f"all{percent}"

dataset = _process_data(dataset_name, split)
print(
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Duplicate print statement.

references = []
raw_hypotheses = []
print(f"Length of Evaluation dataset is {len(dataset)}")

for example in dataset:
for i,example in enumerate(dataset):
print(i)
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Do we need this print statement?

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5 participants