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bert_model.py Normal file
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"""
A PyTorch implementation of Google's BERT Model.
From "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding"
By Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova
Link: http://arxiv.org/abs/1810.04805
Adapted from HuggingFace's OpenAI PyTorch code and its adaptation by AllenNLP.
"""
# pylint: disable=invalid-name,arguments-differ
from typing import NamedTuple, List
import copy
import io
import json
import logging
import math
import pathlib
import re
import tarfile
import numpy as np
import torch
from torch.nn import Parameter
# pylint: disable=line-too-long
_PARAMETER_NAMES = ["model/we:0",
"model/h0/attn/c_attn/w:0", "model/h0/attn/c_attn/b:0", "model/h0/attn/c_proj/w:0",
"model/h0/attn/c_proj/b:0", "model/h0/ln_1/g:0", "model/h0/ln_1/b:0", "model/h0/mlp/c_fc/w:0",
"model/h0/mlp/c_fc/b:0", "model/h0/mlp/c_proj/w:0", "model/h0/mlp/c_proj/b:0", "model/h0/ln_2/g:0",
"model/h0/ln_2/b:0", "model/h1/attn/c_attn/w:0", "model/h1/attn/c_attn/b:0", "model/h1/attn/c_proj/w:0",
"model/h1/attn/c_proj/b:0", "model/h1/ln_1/g:0", "model/h1/ln_1/b:0", "model/h1/mlp/c_fc/w:0",
"model/h1/mlp/c_fc/b:0", "model/h1/mlp/c_proj/w:0", "model/h1/mlp/c_proj/b:0", "model/h1/ln_2/g:0",
"model/h1/ln_2/b:0", "model/h2/attn/c_attn/w:0", "model/h2/attn/c_attn/b:0", "model/h2/attn/c_proj/w:0",
"model/h2/attn/c_proj/b:0", "model/h2/ln_1/g:0", "model/h2/ln_1/b:0", "model/h2/mlp/c_fc/w:0",
"model/h2/mlp/c_fc/b:0", "model/h2/mlp/c_proj/w:0", "model/h2/mlp/c_proj/b:0", "model/h2/ln_2/g:0",
"model/h2/ln_2/b:0", "model/h3/attn/c_attn/w:0", "model/h3/attn/c_attn/b:0", "model/h3/attn/c_proj/w:0",
"model/h3/attn/c_proj/b:0", "model/h3/ln_1/g:0", "model/h3/ln_1/b:0", "model/h3/mlp/c_fc/w:0",
"model/h3/mlp/c_fc/b:0", "model/h3/mlp/c_proj/w:0", "model/h3/mlp/c_proj/b:0", "model/h3/ln_2/g:0",
"model/h3/ln_2/b:0", "model/h4/attn/c_attn/w:0", "model/h4/attn/c_attn/b:0", "model/h4/attn/c_proj/w:0",
"model/h4/attn/c_proj/b:0", "model/h4/ln_1/g:0", "model/h4/ln_1/b:0", "model/h4/mlp/c_fc/w:0",
"model/h4/mlp/c_fc/b:0", "model/h4/mlp/c_proj/w:0", "model/h4/mlp/c_proj/b:0", "model/h4/ln_2/g:0",
"model/h4/ln_2/b:0", "model/h5/attn/c_attn/w:0", "model/h5/attn/c_attn/b:0", "model/h5/attn/c_proj/w:0",
"model/h5/attn/c_proj/b:0", "model/h5/ln_1/g:0", "model/h5/ln_1/b:0", "model/h5/mlp/c_fc/w:0",
"model/h5/mlp/c_fc/b:0", "model/h5/mlp/c_proj/w:0", "model/h5/mlp/c_proj/b:0", "model/h5/ln_2/g:0",
"model/h5/ln_2/b:0", "model/h6/attn/c_attn/w:0", "model/h6/attn/c_attn/b:0", "model/h6/attn/c_proj/w:0",
"model/h6/attn/c_proj/b:0", "model/h6/ln_1/g:0", "model/h6/ln_1/b:0", "model/h6/mlp/c_fc/w:0",
"model/h6/mlp/c_fc/b:0", "model/h6/mlp/c_proj/w:0", "model/h6/mlp/c_proj/b:0", "model/h6/ln_2/g:0",
"model/h6/ln_2/b:0", "model/h7/attn/c_attn/w:0", "model/h7/attn/c_attn/b:0", "model/h7/attn/c_proj/w:0",
"model/h7/attn/c_proj/b:0", "model/h7/ln_1/g:0", "model/h7/ln_1/b:0", "model/h7/mlp/c_fc/w:0",
"model/h7/mlp/c_fc/b:0", "model/h7/mlp/c_proj/w:0", "model/h7/mlp/c_proj/b:0", "model/h7/ln_2/g:0",
"model/h7/ln_2/b:0", "model/h8/attn/c_attn/w:0", "model/h8/attn/c_attn/b:0", "model/h8/attn/c_proj/w:0",
"model/h8/attn/c_proj/b:0", "model/h8/ln_1/g:0", "model/h8/ln_1/b:0", "model/h8/mlp/c_fc/w:0",
"model/h8/mlp/c_fc/b:0", "model/h8/mlp/c_proj/w:0", "model/h8/mlp/c_proj/b:0", "model/h8/ln_2/g:0",
"model/h8/ln_2/b:0", "model/h9/attn/c_attn/w:0", "model/h9/attn/c_attn/b:0", "model/h9/attn/c_proj/w:0",
"model/h9/attn/c_proj/b:0", "model/h9/ln_1/g:0", "model/h9/ln_1/b:0", "model/h9/mlp/c_fc/w:0",
"model/h9/mlp/c_fc/b:0", "model/h9/mlp/c_proj/w:0", "model/h9/mlp/c_proj/b:0", "model/h9/ln_2/g:0",
"model/h9/ln_2/b:0", "model/h10/attn/c_attn/w:0", "model/h10/attn/c_attn/b:0", "model/h10/attn/c_proj/w:0",
"model/h10/attn/c_proj/b:0", "model/h10/ln_1/g:0", "model/h10/ln_1/b:0", "model/h10/mlp/c_fc/w:0",
"model/h10/mlp/c_fc/b:0", "model/h10/mlp/c_proj/w:0", "model/h10/mlp/c_proj/b:0", "model/h10/ln_2/g:0",
"model/h10/ln_2/b:0", "model/h11/attn/c_attn/w:0", "model/h11/attn/c_attn/b:0", "model/h11/attn/c_proj/w:0",
"model/h11/attn/c_proj/b:0", "model/h11/ln_1/g:0", "model/h11/ln_1/b:0", "model/h11/mlp/c_fc/w:0",
"model/h11/mlp/c_fc/b:0", "model/h11/mlp/c_proj/w:0", "model/h11/mlp/c_proj/b:0", "model/h11/ln_2/g:0",
"model/h11/ln_2/b:0", "model/clf/w:0", "model/clf/b:0"]
# pylint: enable=line-too-long
def gelu(x: torch.Tensor) -> torch.Tensor:
return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
class BERTConfig(NamedTuple):
"""
BERT's hyper-parameters
"""
embedding_dim: int = 768
num_heads: int = 12
dropout: float = 0.1
class LayerNorm(torch.nn.Module):
"""
A layernorm module in the Tensorflow style (with the epsilon inside the square root).
"""
def __init__(self, n_state, e=1e-5):
super().__init__()
self.g = torch.nn.Parameter(torch.ones(n_state))
self.b = torch.nn.Parameter(torch.zeros(n_state))
self.e = e
def forward(self, x):
u = x.mean(-1, keepdim=True)
s = (x - u).pow(2).mean(-1, keepdim=True)
x = (x - u) / torch.sqrt(s + self.e)
return self.g * x + self.b
class Conv1D(torch.nn.Module):
"""
A batched linear layer using torch.addmm
"""
def __init__(self, nf: int, rf: int, nx: int) -> None:
super().__init__()
self.rf = rf
self.nf = nf
w = torch.empty(nx, nf)
torch.nn.init.normal_(w, std=0.02)
self.w = Parameter(w)
self.b = Parameter(torch.zeros(nf))
def forward(self, x: torch.Tensor) -> torch.Tensor:
size_out = x.size()[:-1] + (self.nf,)
x = torch.addmm(self.b, x.view(-1, x.size(-1)), self.w)
x = x.view(*size_out)
return x
class Attention(torch.nn.Module):
"""
A self-attention layer comprising a sequence of:
- a linear layer: instance of the `Conv1D` class,
- spliting the inputs in key, value, query tensors (x.split),
- reshaping key, value, query tensors according to the number of head (self.split_heads)
- appying self attention (self._attn)
- merging back the heads results (self.merge_heads)
- a linear layer: instance of the `Conv1D` class,
- a dropout layer: instance of `torch.nn.Dropout` class.
See above for the details of Conv1D.
"""
def __init__(self,
nx: int,
n_ctx: int,
config: BERTConfig,
scale: bool = False) -> None:
super().__init__()
n_state = nx # in Attention: n_state=768 (nx=n_embd)
# [switch nx => n_state from Block to Attention to keep identical to TF implem]
assert n_state % config.num_heads == 0
self.register_buffer('b', torch.tril(torch.ones(n_ctx, n_ctx)).view(1, 1, n_ctx, n_ctx))
self.n_head = config.num_heads
self.split_size = n_state
self.scale = scale
self.c_attn = Conv1D(n_state * 3, 1, nx)
self.c_proj = Conv1D(n_state, 1, nx)
self.attn_dropout = torch.nn.Dropout(config.dropout)
self.resid_dropout = torch.nn.Dropout(config.dropout)
def _attn(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor) -> torch.Tensor:
w = torch.matmul(q, k)
if self.scale:
w = w / math.sqrt(v.size(-1))
w = w * self.b + -1e9 * (1 - self.b) # TF implem method: mask_attn_weights
w = torch.nn.Softmax(dim=-1)(w)
w = self.attn_dropout(w)
return torch.matmul(w, v)
def merge_heads(self, x: torch.Tensor):
# pylint: disable=no-self-use
x = x.permute(0, 2, 1, 3).contiguous()
new_x_shape = x.size()[:-2] + (x.size(-2) * x.size(-1),)
return x.view(*new_x_shape) # in Tensorflow implem: fct merge_states
def split_heads(self, x: torch.Tensor, k: bool = False):
new_x_shape = x.size()[:-1] + (self.n_head, x.size(-1) // self.n_head)
x = x.view(*new_x_shape) # in Tensorflow implem: fct split_states
if k:
return x.permute(0, 2, 3, 1)
else:
return x.permute(0, 2, 1, 3)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.c_attn(x)
query, key, value = x.split(self.split_size, dim=2)
query = self.split_heads(query)
key = self.split_heads(key, k=True)
value = self.split_heads(value)
a = self._attn(query, key, value)
a = self.merge_heads(a)
a = self.c_proj(a)
a = self.resid_dropout(a)
return a
class MLP(torch.nn.Module):
"""
A multi-layer perceptron layer comprising a sequence of:
- a linear layer: instance of the `Conv1D` class,
- an activation function: the `gelu` function,
- another linear layer: instance of the `Conv1D` class,
- a dropout layer: instance of `torch.nn.Dropout` class.
See above for the details of Conv1D and the gelu function.
"""
def __init__(self, n_state: int, config: BERTConfig) -> None: # in MLP: n_state=3072 (4 * n_embd)
super().__init__()
self.c_fc = Conv1D(n_state, 1, config.embedding_dim)
self.c_proj = Conv1D(config.embedding_dim, 1, n_state)
self.act = gelu
self.dropout = torch.nn.Dropout(config.dropout)
def forward(self, x: torch.Tensor) -> torch.Tensor:
h = self.act(self.c_fc(x))
h2 = self.c_proj(h)
return self.dropout(h2)
class Block(torch.nn.Module):
"""
A Transformer Block comprising a sequence of:
- a self-attention layer: instance of the `Attention` class,
- a Layer Normalization layer: instance of the `LayerNorm` class,
- a Multi-layer perceptron layer: instance of the `MLP` class,
- another Layer Normalization layer: instance of the `LayerNorm` class.
See above for the details of these classes.
"""
def __init__(self,
n_ctx: int,
config: BERTConfig,
scale: bool = False) -> None:
super().__init__()
nx = config.embedding_dim
self.attn = Attention(nx, n_ctx, config, scale)
self.ln_1 = LayerNorm(nx)
self.mlp = MLP(4 * nx, config)
self.ln_2 = LayerNorm(nx)
def forward(self, x: torch.Tensor) -> torch.Tensor:
a = self.attn(x)
n = self.ln_1(x + a)
m = self.mlp(n)
h = self.ln_2(n + m)
return h
class BERT(torch.nn.Module):
"""
Google's BERT Model.
Default parameters are the ones for Google's pretrained model.
Parameters
----------
vocab_size: ``int`` (optional, default: 40478)
The size of the vocabulary (number of byte pair embeddings)
excluding the n_special embeddings (if any), and the positional embeddings.
n_ctx: ``int`` (optional, default: 512)
The number of positional encodings to use for evaluation.
embedding_dim: ``int`` (optional, default: 768)
The dimension of the output embeddings.
num_heads: ``int`` (optional, default: 12)
How many "heads" the attention has.
num_layers: ``int`` (optional, default: 12)
How many layers of "blocks" the transformer has.
dropout_probability: ``float`` (optional, default: 0.1)
Dropout for all layers.
model_path: ``str`` (optional, default: ``None``)
A tar.gz file containing serialized model weights. If supplied,
the weights will be loaded from that file.
requires_grad: ``bool`` (optional, default: ``False``)
If true, the transformer will be fine-tuneable.
n_special: ``int`` (optional, default: ``-1``)
The number of special tokens added to the byte pair vocabulary
"""
def __init__(self,
vocab_size: int = 40478,
n_ctx: int = 512,
embedding_dim: int = 768,
num_heads: int = 12,
num_layers: int = 12,
dropout_probability: float = 0.1,
model_path: str = None,
requires_grad: bool = False,
n_special: int = -1) -> None:
super().__init__()
config = BERTConfig(
embedding_dim,
num_heads,
embedding_dropout_probability,
attention_dropout_probability,
residual_dropout_probability,
activation_function,
)
# the embedding size is vocab_size + n_special embeddings + n_ctx
embedding_size = vocab_size + max(n_special, 0) + n_ctx
self.vocab_size = embedding_size
self.n_ctx = n_ctx
self.n_special = n_special
self.num_output_layers = 1 + num_layers
self.embed = torch.nn.Embedding(embedding_size, embedding_dim)
self.drop = torch.nn.Dropout(embedding_dropout_probability)
block = Block(n_ctx, config, scale=True)
self.h = torch.nn.ModuleList([copy.deepcopy(block) for _ in range(num_layers)])
self.decoder = torch.nn.Linear(embedding_dim, embedding_size, bias=False)
self.decoder.weight = self.embed.weight # Tied weights
# To reproduce the noise_shape parameter of TF implementation
torch.nn.init.normal_(self.embed.weight, std=0.02)
for parameter in self.parameters():
parameter.requires_grad = requires_grad
if model_path:
self.load_weights(model_path, n_special=n_special, n_ctx=n_ctx)
def forward(self, x: torch.Tensor) -> List[torch.Tensor]:
#x = x.view(-1, x.size(2), x.size(3))
# x is (batch_size, sequence_length) tensor of byte-pair ids
# e is (batch_size, sequence_length, 2, embedding_dim) tensor of embeddings
e = self.embed(x)
# h is (batch_size, sequence_length, embedding_dim)
h = e.sum(dim=2)
all_layers = [h]
for block in self.h:
h = block(h)
all_layers.append(h)
# result is list of (batch_size, sequence_length, embedding_dim)
return all_layers
def load_weights(self,
bert_model_path: str,
n_ctx: int = -1,
n_special: int = -1,
n_transfer: int = 12,
n_embd: int = 768,
names: List[str] = _PARAMETER_NAMES) -> None:
# pylint: disable=dangerous-default-value
logger.info(f"loading weights from {bert_model_path}")
# if `file_path` is a URL, redirect to the cache
with tarfile.open(bert_model_path) as tmp:
num_params_files = len([member for member in tmp.getmembers() if member.name.endswith('.npy')])
shapesfile = tmp.extractfile('model/params_shapes.json')
if shapesfile:
shapes = json.loads(shapesfile.read())
else:
raise ConfigurationError("unable to find model/params_shapes.json in the archive")
# numpy can't read from a tarfile directly, so we need a workaround
# https://github.com/numpy/numpy/issues/7989#issuecomment-341656702
init_params: List[np.ndarray] = []
for n in range(num_params_files):
array_file = io.BytesIO()
array_file.write(tmp.extractfile(f'model/params_{n}.npy').read())
array_file.seek(0)
# each np.load is a (11653478,) numpy array
init_params.append(np.load(array_file))
# init_params is a list of 10 arrays of size (11653578,)
# shapes are [[512, 768], [40478, 768], [1, 768, 2304], [2304], ... # 146 elts
# products are [512 * 768, 40478 * 768, ...]
# offsets is [512 * 768, 512 * 768 + 40478 * 768, ...]
offsets = np.cumsum([np.prod(shape) for shape in shapes])
# split into the 146 subarrays corresponding to shapes
init_params = np.split(np.concatenate(init_params, 0), offsets)[:-1]
# reshape
init_params = [param.reshape(shape) for param, shape in zip(init_params, shapes)]
# truncate if necessary
if n_ctx > 0:
# positional embeddings?
# init_params[0] is (512, 768) = (max_chars, embedding_dim)
init_params[0] = init_params[0][:n_ctx]
# combine init_params[1] and init_params[0]
if n_special > 0:
# init_params[1] is (40478, 768)
# special is (n_special, 768)
# init_params[0] is (512, 768)
# result is (40990 + n_special, 768)
init_params[0] = np.concatenate(
[init_params[1],
(np.random.randn(n_special, n_embd) * 0.02).astype(np.float32),
init_params[0]],
0
)
else:
# result is (40990, 768)
init_params[0] = np.concatenate([init_params[1], init_params[0]], 0)
del init_params[1]
# number of dimensions to transfer, 12 per layer, plus one extra
if n_transfer == -1:
n_transfer = 0
else:
n_transfer = 1 + n_transfer * 12
# squeeze?
init_params = [arr.squeeze() for arr in init_params]
# embedding.weight is (vocab_size, embedding_dim)
# make sure init_params[0] has the same shape
try:
assert self.embed.weight.shape == init_params[0].shape
except AssertionError as e:
e.args += (self.embed.weight.shape, init_params[0].shape)
raise
# and then assign it
self.embed.weight.data = torch.from_numpy(init_params[0])
self.decoder.weight = self.embed.weight
# for each (name, array) pair to transfer over
for name, ip in zip(names[1:n_transfer], init_params[1:n_transfer]):
# "model/h0/attn/c_attn/w:0"
name = name[6:] # "h0/attn/c_attn/w:0"
assert name[-2:] == ":0"
name = name[:-2] # "h0/attn/c_attn/w"
name_parts = name.split('/') # ['h0', 'attn', 'c_attn', 'w']
pointer = self
for m_name in name_parts:
if re.fullmatch(r'[A-Za-z]+\d+', m_name):
l = re.split(r'(\d+)', m_name) # ['h', '0', '']
else:
l = [m_name] # ['attn']
pointer = getattr(pointer, l[0])
if len(l) >= 2:
num = int(l[1])
pointer = pointer[num]
try:
assert pointer.shape == ip.shape
except AssertionError as e:
e.args += (pointer.shape, ip.shape)
raise
pointer.data = torch.from_numpy(ip) # pylint: disable=attribute-defined-outside-init
def dump_weights(self,
output_dir: str,
num_pieces: int = 10) -> None:
output_path = pathlib.Path(output_dir) / 'model'
output_path.mkdir(exist_ok=True, parents=True) # pylint: disable=no-member
named_parameters = list(self.named_parameters())
# embedding weights get special treatment
_, array = named_parameters[0]
num_bpe = self.vocab_size - self.n_ctx
byte_pair_embeddings = array[:num_bpe]
positional_embeddings = array[num_bpe:]
arrays = [positional_embeddings.numpy().ravel(), byte_pair_embeddings.numpy().ravel()]
shapes = [positional_embeddings.shape, byte_pair_embeddings.shape]
names = ["model/we:0"]
for param_name, tensor in named_parameters[1:]:
param_name = f'h{param_name}' # 'h0.attn.c_attn.w'
parts = param_name.split(".") # ['h0', 'attn', 'c_attn', 'w']
name = "model/" + '/'.join(parts) + ':0' # 'model/h0/attn/c_attn/w:0'
array = tensor.numpy().ravel()
arrays.append(array)
shapes.append(list(tensor.shape))
names.append(name)
# write out the arrays
big_array = np.concatenate(arrays)
total_size = len(big_array)
batch_size = math.ceil(total_size / num_pieces)
for i in range(num_pieces):
filename = output_path / f"params_{i}.npy"
start = i * batch_size
end = start + batch_size
subarray = big_array[start:end]
np.save(filename, subarray)
# write out the shapes
with open(output_path / 'params_shapes.json', 'w') as shapes_file:
json.dump(shapes, shapes_file)

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"""
Prepare input data for Google's BERT Model.
Contains some functions from tensor2tensor library: https://github.com/tensorflow/tensor2tensor
"""
from typing import NamedTuple, List, Union, Tuple
TokenizedSentence = List[str]
TokenizedInput = Union[Tuple[TokenizedSentence, TokenizedSentence], TokenizedSentence]
class DataProcessor():
def __init__(self, vocab_path):
self.encoder_file_path = encoder_file_path
self.token_indexer = json.load(open(vocab_path))
def tokenize(text):
"""Encode a unicode string as a list of tokens.
Args:
text: a unicode string
Returns:
a list of tokens as Unicode strings
"""
if not text:
return []
ret = []
token_start = 0
# Classify each character in the input string
is_alnum = [c in _ALPHANUMERIC_CHAR_SET for c in text]
for pos in range(1, len(text)):
if is_alnum[pos] != is_alnum[pos - 1]:
token = text[token_start:pos]
if token != u" " or token_start == 0:
ret.append(token)
token_start = pos
final_token = text[token_start:]
ret.append(final_token)
return ret
def detokenize(tokens):
"""Decode a list of tokens to a unicode string.
Args:
tokens: a list of Unicode strings
Returns:
a unicode string
"""
token_is_alnum = [t[0] in _ALPHANUMERIC_CHAR_SET for t in tokens]
ret = []
for i, token in enumerate(tokens):
if i > 0 and token_is_alnum[i - 1] and token_is_alnum[i]:
ret.append(u" ")
ret.append(token)
return "".join(ret)
def encode(input_sentences: List[TokenizedInput]) -> np.array:
""" Prepare a torch.Tensor of inputs for BERT model from a string.
Args:
input_sentences: list of
- pairs of tokenized sentences (sentence_A, sentence_B) or
- tokenized sentences (will be considered as sentence_A only)
Return:
Numpy array of formated inputs for BERT model
"""
batch_size = sum(min(len(x), n_perso_permute) for x in X1)
input_mask = np.zeros((n_batch, n_cands, n_ctx), dtype=np.float32)
input_array = np.zeros((n_batch, n_cands, n_ctx, 3), dtype=np.int32)
i = 0
for tokenized_input in input_sentences:
x1j, lxj, lperso, lhisto, dialog_embed = format_transformer_input(x1, x2, xcand_j, text_encoder,
dialog_embed_mode, max_len=max_len,
add_start_stop=True)
lmj = len(xcand_j[:max_len]) + 1
xmb[i, j, :lxj, 0] = x1j
if dialog_embed_mode == 1 or dialog_embed_mode == 2:
xmb[i, j, :lxj, 2] = dialog_embed
mmb[i, j, :lxj] = 1
if fix_lm_index: # Take one before so we don't predict from classify token...
mmb_eval[i, j, (lxj-lmj-1):lxj-1] = 1 # This one only mask the response so we get the perplexity on the response only
else:
mmb_eval[i, j, (lxj-lmj):lxj] = 1 # This one only mask the response so we get the perplexity on the response only
xmb[i, j, :, 1] = np.arange(n_vocab+n_special, n_vocab+n_special+n_ctx)
i += 1
return input_array, input_mask

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echo "=== Downloading BERT pre-trained weights ==="
echo "---"
wget --quiet --continue http://www.fit.vutbr.cz/~imikolov/rnnlm/simple-examples.tgz
tar -xzf simple-examples.tgz
rm -rf simple-examples.tgz

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"""
Show how to use HuggingFace's PyTorch implementation of Google's BERT Model.
"""
from .bert_model import BERT
from .prepare_inputs import DataPreprocessor
bert_model = BERT()
bert_model.load_from('.')
data_processor = DataProcessor(encoder_file_path='.')
input_sentence = "We are playing with the BERT model."
print("BERT inputs: {}".format(input_sentence))
tensor_input = data_processor.encode(input_sentence)
tensor_output = bert_model(prepared_input)
output_sentence = data_processor.decode(tensor_output)
print("BERT predicted: {}".format(output_sentence))