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