def construct_requests(self, doc, ctx):
""" Uses RequestFactory to construct Requests and returns an iterable of
Requests which will be sent to the LM.
:param doc:
The document as returned from training_docs, validation_docs, or test_docs.
:param ctx: str
The context string, generated by fewshot_context. This includes the natural
language description, as well as the few shot examples, and the question
part of the document for `doc`.
"""
return rf.greedy_until(ctx, ["\n"])
def construct_requests(self, doc, ctx):
"""Uses RequestFactory to construct Requests and returns an iterable of
Requests which will be sent to the LM.
:param doc:
The document as returned from training_docs, validation_docs, or test_docs.
:param ctx: str
The context string, generated by fewshot_context. This includes the natural
language description, as well as the few shot examples, and the question
part of the document for `doc`.
"""
# TODO: Find a way to cap the number of generated tokens to `50` as in the official implementation.
completion = rf.greedy_until(ctx, ["."])
return completion
def construct_requests(self, doc, ctx):
"""Uses RequestFactory to construct Requests and returns an iterable of
Requests which will be sent to the LM.
:param doc:
The document as returned from training_docs, validation_docs, or test_docs.
:param ctx: str
The context string, generated by fewshot_context. This includes the natural
language description, as well as the few shot examples, and the question
part of the document for `doc`.
"""
# NOTE: The paper implements "verifiers" that assign a score to multiple
# solutions and output the highest ranked solution.
completion = rf.greedy_until(ctx, ["\n"])
return completion
def construct_requests(self, doc, ctx):
"""Uses RequestFactory to construct Requests and returns an iterable of
Requests which will be sent to the LM.
:param doc:
The document as returned from training_docs, validation_docs, or test_docs.
:param ctx: str
The context string, generated by fewshot_context. This includes the natural
language description, as well as the few shot examples, and the question
part of the document for `doc`.
"""
# unanswerable = rf.loglikelihood(ctx, " " + "unanswerable")
if doc["answer_type"] in ("free form answer"):
return [rf.greedy_until(ctx, ["\n"])]
elif doc["answer_type"] in ("bool"):
ll_yes, _ = rf.loglikelihood(ctx, " yes")
ll_no, _ = rf.loglikelihood(ctx, " no")
return [ll_yes, ll_no]
else:
return []
def construct_requests(self, doc, ctx):
return rf.greedy_until(ctx, ["\n"])
def construct_requests(self, doc, ctx):
completion = rf.greedy_until(ctx, ["\n"])
return completion
