def gensummary_gpt2(template_vec,
ge,
vocab,
LMModel,
word_list,
subvocab,
clustermask=None,
mono=True,
renorm=True,
temperature=1,
bpe2word='last',
max_step = 20,
beam_width = 10,
beam_width_start = 10,
alpha=0.1,
alpha_start=0.1,
begineos=True,
stopbyLMeos=False,
devid=0,
**kwargs):
"""
Unsupervised sentence summary generation using beam search, by contextual matching and a summary style language model.
The contextual matching here is on top of pretrained ELMo embeddings.
Input:
template_vec: forward only ELMo embeddings of the source sentence. 'torch.Tensor' of size (3, seq_len, 512).
ge: 'gpt2_sequential_embedder.GPT2Embedder' object.
vocab: 'torchtext.vocab.Vocab' object. Should be the same as is used for the pretrained language model.
LMModel: a pretrained language model on the summary sentences.
word_list: a list of words in the vocabulary to work with. 'List'.
subvocab: 'torch.LongTensor' consisting of the indices of the words corresponding to 'word_list'.
clustermask: a binary mask for each of the sub-vocabulary word. 'torch.ByteTensor' of size (len(sub-vocabulary), len(vocabulary)). Default:None.
mono: whether to keep monotonicity contraint. Default: True.
renorm: whether to renormalize the probabilities over the sub-vocabulary. Default: True.
Temperature: temperature applied to the softmax in the language model. Default: 1.
bpe2word: how to turn the BPE vectors into word vectors. Choose from ['last', 'avg']. Default: 'last'.
max_step: maximum number of beam steps.
beam_width: beam width.
beam_width_start: beam width of the first step.
alpha: the amount of language model part used for scoring. The score is: (1 - \alpha) * similarity_logscore + \alpha * LM_logscore.
begineos: whether to begin with the special '<eos>' token as is trained in the language model. Note that ELMo has its own special beginning token. Default: True.
stopbyLMeos: whether to stop a sentence solely by the language model predicting '<eos>' as the top possibility. Default: False.
devid: device id to run the algorithm and LSTM language models. 'int', default: 0. -1 for cpu.
**kwargs: other arguments input to function <Beam.beamstep>.
E.g. normalized: whether to normalize the dot product when calculating the similarity, which makes it cosine similarity. Default: True.
ifadditive: whether to use an additive model on mixing the probability scores. Default: False.
Output:
beam: 'Beam' object, recording all the generated sequences.
"""
device = 'cpu' if devid == -1 else f'cuda:{devid}'
# Beam Search: initialization
if begineos:
beam = Beam(1, vocab, init_ids=[vocab.stoi['<eos>']], device=device,
sim_score=0, lm_score=0, lm_state=None, gpt2_state=None, align_loc=None)
else:
beam = Beam(1, vocab, init_ids=[None], device=device,
sim_score=0, lm_score=0, lm_state=None, gpt2_state=None, align_loc=None)
# first step: start with 'beam_width_start' best matched words
beam.beamstep(beam_width_start,
beam.combscoreK_GPT2,
template_vec=template_vec,
ge=ge,
LMModel=LMModel,
word_list=word_list,
subvocab=subvocab,
clustermask=clustermask,
alpha=alpha_start,
renorm=renorm,
temperature=temperature,
bpe2word=bpe2word,
normalized=True,
ifadditive=False,
**kwargs)
# run beam search, until all sentences hit <EOS> or max_step reached
for s in range(max_step):
print(f'beam step {s+1} ' + '-' * 50 + '\n')
beam.beamstep(beam_width,
beam.combscoreK_GPT2,
template_vec=template_vec,
ge=ge,
LMModel=LMModel,
word_list=word_list,
subvocab=subvocab,
clustermask=clustermask,
mono=mono,
alpha=alpha,
renorm=renorm,
temperature=temperature,
stopbyLMeos=stopbyLMeos,
bpe2word=bpe2word,
normalized=True,
ifadditive=False,
**kwargs)
# all beams reach termination
if beam.endall:
break
return beam
def gensummary_elmo(template_vec,
ee,
vocab,
LMModel,
word_list,
subvocab,
clustermask=None,
mono=True,
renorm=True,
temperature=1,
elmo_layer='avg',
max_step=20,
beam_width=10,
beam_width_start=10,
alpha=0.1,
alpha_start=0.1,
begineos=True,
stopbyLMeos=False,
devid=0,
**kwargs):
"""
Unsupervised sentence summary generation using beam search, by contextual matching and a summary style language model.
The contextual matching here is on top of pretrained ELMo embeddings.
Input:
- template_vec (torch.Tensor): forward only ELMo embeddings of the source sentence.
'torch.Tensor' of size (3, seq_len, 512).
- ee (elmo_sequential_embedder.ElmoEmbedderForward): 'elmo_sequential_embedder.ElmoEmbedderForward' object.
- vocab (torchtext.vocab.Vocab): 'torchtext.vocab.Vocab' object. Should be the same as is used for the
pretrained language model.
- LMModel (user defined torch.nn.Module): a pretrained language model on the summary sentences.
- word_list (list): a list of words in the vocabulary to work with. 'List'.
- subvocab (torch.LongTensor): 'torch.LongTensor' consisting of the indices of the words corresponding
to `word_list`.
- clustermask (torch.ByteTensor): a binary mask for each of the sub-vocabulary word.
'torch.ByteTensor' of size (len(sub-vocabulary), len(vocabulary)). Default:None.
- mono (bool): whether to keep monotonicity contraint. Default: True.
- renorm (bool): whether to renormalize the probabilities over the sub-vocabulary. Default: True.
- temperature (float): temperature applied to the softmax in the language model. Default: 1.
- elmo_layer (str): which ELMo layer to use as the word type representation.
Choose from ['avg', 'cat', 'bot', 'mid', 'top']. Default: 'avg'.
- max_step (int): maximum number of beam steps.
- beam_width (int): beam width.
- beam_width_start (int): beam width of the first step.
- alpha (float): the amount of language model part used for scoring. The score is:
(1 - \alpha) * similarity_logscore + \alpha * LM_logscore.
- alpha_start (float): the amount of language model part used for scoring, only for the first step.
- begineos (bool): whether to begin with the special '<eos>' token as is trained in the language model.
Note that ELMo has its own special beginning token. Default: True.
- stopbyLMeos (bool): whether to stop a sentence solely by the language model predicting '<eos>' as the
top possibility. Default: False.
- devid (int): device id to run the algorithm and LSTM language models. 'int', default: 0. -1 for cpu.
**kwargs: other arguments input to function <Beam.beamstep>.
E.g. - normalized (bool): whether to normalize the dot product when calculating the similarity,
which makes it cosine similarity. Default: True.
- ifadditive (bool): whether to use an additive model on mixing the probability scores. Default: False.
Output:
- beam (beam_search.Beam): 'Beam' object, recording all the generated sequences.
"""
device = 'cpu' if devid == -1 else f'cuda:{devid}'
# Beam Search: initialization
if begineos:
beam = Beam(1,
vocab,
init_ids=[vocab.stoi['<eos>']],
device=device,
sim_score=0,
lm_score=0,
lm_state=None,
elmo_state=None,
align_loc=None)
else:
beam = Beam(1,
vocab,
init_ids=[None],
device=device,
sim_score=0,
lm_score=0,
lm_state=None,
elmo_state=None,
align_loc=None)
# first step: start with 'beam_width_start' best matched words
beam.beamstep(
beam_width_start,
beam.combscoreK,
template_vec=template_vec,
ee=ee,
LMModel=LMModel,
word_list=word_list,
subvocab=subvocab,
clustermask=clustermask,
alpha=alpha_start,
renorm=renorm,
temperature=temperature,
elmo_layer=elmo_layer,
# normalized=True,
# ifadditive=False,
**kwargs)
# run beam search, until all sentences hit <EOS> or max_step reached
for s in range(max_step):
print(f'beam step {s + 1} ' + '-' * 50 + '\n')
beam.beamstep(
beam_width,
beam.combscoreK,
template_vec=template_vec,
ee=ee,
LMModel=LMModel,
word_list=word_list,
subvocab=subvocab,
clustermask=clustermask,
mono=mono,
alpha=alpha,
renorm=renorm,
temperature=temperature,
stopbyLMeos=stopbyLMeos,
elmo_layer=elmo_layer,
# normalized=True,
# ifadditive=False,
**kwargs)
# all beams reach termination
if beam.endall:
break
return beam
