Esempio n. 1
0
        decode_span_type='FOS',
        decode_span=span,
        mask_propmt_text='Field of Study:',
        debug=False)
    print('%s probability: %.4f' % (span.ljust(30), span_prob))
print()

# decode a list of Field-Of-Study using beam search
concepts = []
print('=== Generated FOS ===')
for i in range(4):
    candidates = []
    for span_length in range(1, 5):
        results = model.decode_beamsearch(title=title,
                                          abstract=abstract,
                                          authors=[],
                                          concepts=concepts,
                                          decode_span_type='FOS',
                                          decode_span_length=span_length,
                                          beam_width=8,
                                          force_forward=False)
        candidates.append(results[0])
    candidates.sort(key=lambda x: -x[1])
    span, prob = candidates[0]
    print("%2d. %s %s" %
          (i + 1, span,
           colored(
               '[%s]' % (','.join(['%s(%.4f)' % (k, v)
                                   for k, v in candidates])), 'blue')))
    concepts.append(span)
Esempio n. 2
0
        debug=False,
    )
    print("%s probability: %.4f" % (span.ljust(30), span_prob))
print()

# decode a list of Field-Of-Study using beam search
concepts = []
print("=== Generated FOS ===")
for i in range(16):
    candidates = []
    for span_length in range(1, 5):
        results = model.decode_beamsearch(
            title=title,
            abstract=abstract,
            authors=[],
            concepts=concepts,
            decode_span_type="FOS",
            decode_span_length=span_length,
            beam_width=8,
            force_forward=False,
        )
        candidates.append(results[0])
    candidates.sort(key=lambda x: -x[1])
    span, prob = candidates[0]
    print("%2d. %s %s" %
          (i + 1, span,
           colored(
               "[%s]" % (",".join(["%s(%.4f)" % (k, v)
                                   for k, v in candidates])), "blue")))
    concepts.append(span)
Esempio n. 3
0
        decode_span=span,
        mask_propmt_text="Field of Study:",
        debug=False,
    )
    print("%s probability: %.4f" % (span.ljust(30), span_prob))
print()

# decode a list of Field-Of-Study using beam search
concepts = []
print("=== Generated FOS ===")
for i in range(4):
    candidates = []
    for span_length in range(1, 5):
        results = model.decode_beamsearch(
            title=title,
            abstract=abstract,
            authors=[],
            concepts=concepts,
            decode_span_type="FOS",
            decode_span_length=span_length,
            beam_width=8,
            force_forward=False,
        )
        candidates.append(results[0])
    candidates.sort(key=lambda x: -x[1])
    span, prob = candidates[0]
    print(
        "%2d. %s %s" % (i + 1, span, colored("[%s]" % (",".join(["%s(%.4f)" % (k, v) for k, v in candidates])), "blue"))
    )
    concepts.append(span)