def __init__(self, opt):
super().__init__(opt)
# initialize from vocab path
warn_once(
'WARNING: BERT uses a Hugging Face tokenizer; ParlAI dictionary args are ignored'
)
download(opt['datapath'])
vocab_path = os.path.join(opt['datapath'], 'models', 'bert_models',
VOCAB_PATH)
self.tokenizer = BertTokenizer.from_pretrained(vocab_path)
self.start_token = '[CLS]'
self.end_token = '[SEP]'
self.null_token = '[PAD]'
self.start_idx = self.tokenizer.convert_tokens_to_ids(
['[CLS]'])[0] # should be 101
self.end_idx = self.tokenizer.convert_tokens_to_ids(
['[SEP]'])[0] # should be 102
self.pad_idx = self.tokenizer.convert_tokens_to_ids(
['[PAD]'])[0] # should be 0
# set tok2ind for special tokens
self.tok2ind[self.start_token] = self.start_idx
self.tok2ind[self.end_token] = self.end_idx
self.tok2ind[self.null_token] = self.pad_idx
# set ind2tok for special tokens
self.ind2tok[self.start_idx] = self.start_token
self.ind2tok[self.end_idx] = self.end_token
self.ind2tok[self.pad_idx] = self.null_token
def __init__(self, opt, shared=None):
# download pretrained models
download(opt['datapath'])
self.pretrained_path = os.path.join(opt['datapath'], 'models',
'bert_models', MODEL_PATH)
opt['pretrained_path'] = self.pretrained_path
super().__init__(opt, shared)
def __init__(self, opt):
super().__init__(opt)
# initialize from vocab path
download(opt['datapath'])
vocab_path = os.path.join(opt['datapath'], 'models', 'bert_models', VOCAB_PATH)
self.tokenizer = BertTokenizer.from_pretrained(vocab_path)
self.start_token = '[CLS]'
self.end_token = '[SEP]'
self.null_token = '[PAD]'
self.start_idx = self.tokenizer.convert_tokens_to_ids(['[CLS]'])[
0
] # should be 101
self.end_idx = self.tokenizer.convert_tokens_to_ids(['[SEP]'])[
0
] # should be 102
self.pad_idx = self.tokenizer.convert_tokens_to_ids(['[PAD]'])[0] # should be 0
# set tok2ind for special tokens
self.tok2ind[self.start_token] = self.start_idx
self.tok2ind[self.end_token] = self.end_idx
self.tok2ind[self.null_token] = self.pad_idx
# set ind2tok for special tokens
self.ind2tok[self.start_idx] = self.start_token
self.ind2tok[self.end_idx] = self.end_token
self.ind2tok[self.pad_idx] = self.null_token
def __init__(self, opt):
super().__init__(opt)
# initialize from vocab path
warn_once(
"WARNING: BERT uses a Hugging Face tokenizer; ParlAI dictionary args are ignored"
)
download(opt["datapath"])
vocab_path = PathManager.get_local_path(
os.path.join(opt["datapath"], "models", "bert_models", VOCAB_PATH))
self.tokenizer = BertTokenizer.from_pretrained(vocab_path)
self.start_token = "[CLS]"
self.end_token = "[SEP]"
self.null_token = "[PAD]"
self.start_idx = self.tokenizer.convert_tokens_to_ids(
["[CLS]"])[0] # should be 101
self.end_idx = self.tokenizer.convert_tokens_to_ids(
["[SEP]"])[0] # should be 102
self.pad_idx = self.tokenizer.convert_tokens_to_ids(
["[PAD]"])[0] # should be 0
# set tok2ind for special tokens
self.tok2ind[self.start_token] = self.start_idx
self.tok2ind[self.end_token] = self.end_idx
self.tok2ind[self.null_token] = self.pad_idx
# set ind2tok for special tokens
self.ind2tok[self.start_idx] = self.start_token
self.ind2tok[self.end_idx] = self.end_token
self.ind2tok[self.pad_idx] = self.null_token
def __init__(self, opt, shared=None):
# download pretrained models
download(opt['datapath'])
self.pretrained_path = os.path.join(opt['datapath'], 'models',
'bert_models', MODEL_PATH)
opt['pretrained_path'] = self.pretrained_path
self.add_cls_token = opt.get('add_cls_token', True)
self.sep_last_utt = opt.get('sep_last_utt', False)
super().__init__(opt, shared)
def __init__(self, opt, shared=None):
# download pretrained models
download(opt["datapath"])
self.pretrained_path = PathManager.get_local_path(
os.path.join(opt["datapath"], "models", "bert_models", MODEL_PATH))
opt["pretrained_path"] = self.pretrained_path
self.add_cls_token = opt.get("add_cls_token", True)
self.sep_last_utt = opt.get("sep_last_utt", False)
super().__init__(opt, shared)
def __init__(self, opt, shared=None):
# download pretrained models
download(opt['datapath'])
self.pretrained_path = os.path.join(opt['datapath'], 'models',
'bert_models', MODEL_PATH)
super().__init__(opt, shared)
# it's easier for now to use DataParallel when
self.clip = -1
self.NULL_IDX = self.dict.pad_idx
self.START_IDX = self.dict.start_idx
self.END_IDX = self.dict.end_idx
def __init__(self, opt, shared=None):
# download pretrained models
download(opt['datapath'])
self.pretrained_path = os.path.join(opt['datapath'], 'models',
'bert_models', MODEL_PATH)
opt['pretrained_path'] = self.pretrained_path
self.clip = -1
super().__init__(opt, shared)
# it's easier for now to use DataParallel when
self.NULL_IDX = self.dict.pad_idx
self.START_IDX = self.dict.start_idx
self.END_IDX = self.dict.end_idx
# default one does not average
self.rank_loss = torch.nn.CrossEntropyLoss(reduce=True,
size_average=True)
def __init__(self, opt, shared=None):
# download pretrained models
download(opt['datapath'])
self.pretrained_path = os.path.join(opt['datapath'], 'models',
'bert_models', MODEL_PATH)
super().__init__(opt, shared)
# it's easier for now to use DataParallel when
self.data_parallel = opt.get('data_parallel') and self.use_cuda
if self.data_parallel:
self.model = torch.nn.DataParallel(self.model)
if is_distributed():
raise ValueError(
'Cannot combine --data-parallel and distributed mode')
self.clip = -1
self.NULL_IDX = self.dict.pad_idx
self.START_IDX = self.dict.start_idx
self.END_IDX = self.dict.end_idx
def __init__(self, opt, shared=None):
# download pretrained models
download(opt['datapath'])
self.pretrained_path = os.path.join(
opt['datapath'], 'models', 'bert_models', MODEL_PATH
)
opt['pretrained_path'] = self.pretrained_path
self.clip = -1
super().__init__(opt, shared)
# it's easier for now to use DataParallel when
self.data_parallel = opt.get('data_parallel') and self.use_cuda
if self.data_parallel and shared is None:
self.model = torch.nn.DataParallel(self.model)
if is_distributed():
raise ValueError('Cannot combine --data-parallel and distributed mode')
self.NULL_IDX = self.dict.pad_idx
self.START_IDX = self.dict.start_idx
self.END_IDX = self.dict.end_idx
# default one does not average
self.rank_loss = torch.nn.CrossEntropyLoss(reduce=True, size_average=True)
