import numpy as np
import pandas as pd
import torch
from pytorch_pretrained_bert import BertTokenizer, BertModel, BertForTokenClassification
import json
from tqdm import tqdm
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from torch.utils.data import Dataset
import os
import time
# Load pre-trained model (weights)
model = BertForTokenClassification.from_pretrained('bert-base-cased',
num_labels=3)
# Load pre-trained model tokenizer (vocabulary)
tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
# print(model)
print(tokenizer)
print("vocab_size : {}\n".format(len(tokenizer.vocab)))
print(tokenizer.convert_tokens_to_ids(['to', '[PAD]']))
df = pd.read_csv('../input/train.csv')
# print(df)
print("df key :", df.keys())
char_label_maxlen = 0
word_label_maxlen = 0
# check label length at char level
char_length_dic = {}
train_data = TensorDataset(tr_inputs, tr_masks, tr_tags)
train_sampler = RandomSampler(train_data)
train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=bs)
valid_data = TensorDataset(val_inputs, val_masks, val_tags)
valid_sampler = SequentialSampler(valid_data)
valid_dataloader = DataLoader(valid_data, sampler=valid_sampler, batch_size=bs)
model = BertForTokenClassification.from_pretrained(
"bert-base-cased",
num_labels=len(tag2idx),
output_attentions = False,
output_hidden_states = False
)
FULL_FINETUNING = False
if FULL_FINETUNING:
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'gamma', 'beta']
optimizer_grouped_parameters = [
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
'weight_decay_rate': 0.01},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay_rate': 0.0}
]
else:
# Initialize the DataLoader
data_loader = DataLoader(args.data_dir, args.bert_model_dir, params, token_pad_idx=0)
# Load data
test_data = data_loader.load_data('test')
# Specify the test set size
params.test_size = test_data['size']
params.eval_steps = params.test_size // params.batch_size
test_data_iterator = data_loader.data_iterator(test_data, shuffle=False)
logging.info("- done.")
# Define the model
config_path = os.path.join(args.bert_model_dir, 'bert_config.json')
config = BertConfig.from_json_file(config_path)
model = BertForTokenClassification(config, num_labels=len(params.tag2idx))
model.to(params.device)
# Reload weights from the saved file
utils.load_checkpoint(os.path.join(args.model_dir, args.restore_file + '.pth.tar'), model)
if args.fp16:
model.half()
if params.n_gpu > 1 and args.multi_gpu:
model = torch.nn.DataParallel(model)
logging.info("Starting evaluation...")
test_metrics = evaluate(model, test_data_iterator, params, mark='Test', verbose=True)
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument(
"--data_dir",
default='/home/adzuser/user_achyuta/BERT_NER_Test/BERT-NER/NERdata/',
type=str,
required=True,
help=
"The input data dir. Should contain the .tsv files (or other data files) for the task."
)
parser.add_argument(
"--bert_model",
default=None,
type=str,
required=True,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, "
"bert-base-multilingual-cased, bert-base-chinese.")
parser.add_argument("--task_name",
default='NER',
type=str,
required=True,
help="The name of the task to train.")
parser.add_argument(
"--output_dir",
default='ner_output',
type=str,
required=True,
help=
"The output directory where the model predictions and checkpoints will be written."
)
## Other parameters
parser.add_argument(
"--cache_dir",
default="",
type=str,
help=
"Where do you want to store the pre-trained models downloaded from s3")
parser.add_argument(
"--max_seq_length",
default=128,
type=int,
help=
"The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, and sequences shorter \n"
"than this will be padded.")
parser.add_argument("--do_train",
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_eval",
action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument("--do_test",
action='store_true',
help="Whether to run test on the test set.")
parser.add_argument("--do_pred",
action='store_true',
help="Whether to run pred on the pred set.")
parser.add_argument(
"--do_lower_case",
action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument("--train_batch_size",
default=32,
type=int,
help="Total batch size for training.")
parser.add_argument("--eval_batch_size",
default=8,
type=int,
help="Total batch size for eval.")
parser.add_argument("--learning_rate",
default=5e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument(
"--num_train_epochs",
default=10.0, #3.0,
type=float,
help="Total number of training epochs to perform.")
parser.add_argument(
"--warmup_proportion",
default=0.1,
type=float,
help=
"Proportion of training to perform linear learning rate warmup for. "
"E.g., 0.1 = 10%% of training.")
parser.add_argument("--no_cuda",
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
parser.add_argument('--seed',
type=int,
default=42,
help="random seed for initialization")
parser.add_argument('--clip',
type=float,
default=0.5,
help="gradient clipping")
parser.add_argument(
'--gradient_accumulation_steps',
type=int,
default=1,
help=
"Number of updates steps to accumulate before performing a backward/update pass."
)
parser.add_argument(
'--fp16',
action='store_true',
help="Whether to use 16-bit float precision instead of 32-bit")
parser.add_argument(
'--loss_scale',
type=float,
default=0,
help=
"Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
"0 (default value): dynamic loss scaling.\n"
"Positive power of 2: static loss scaling value.\n")
parser.add_argument('--server_ip',
type=str,
default='',
help="Can be used for distant debugging.")
parser.add_argument('--server_port',
type=str,
default='',
help="Can be used for distant debugging.")
parser.add_argument('--text_a', type=str, default='', help="input text_a.")
parser.add_argument('--text_b', type=str, default='', help="input text_b.")
args = parser.parse_args()
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach")
ptvsd.enable_attach(address=(args.server_ip, args.server_port),
redirect_output=True)
ptvsd.wait_for_attach()
processors = {"ner": NerProcessor}
num_labels_task = {"ner": 12}
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available()
and not args.no_cuda else "cpu")
n_gpu = torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
n_gpu = 1
# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.distributed.init_process_group(backend='nccl')
if args.gradient_accumulation_steps < 1:
raise ValueError(
"Invalid gradient_accumulation_steps parameter: {}, should be >= 1"
.format(args.gradient_accumulation_steps))
args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
if not args.do_train and not args.do_eval and not args.do_pred:
raise ValueError(
"At least one of `do_train` or `do_eval` must be True.")
if os.path.exists(args.output_dir) and os.listdir(
args.output_dir) and args.do_train:
raise ValueError(
"Output directory ({}) already exists and is not empty.".format(
args.output_dir))
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
task_name = args.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name]()
num_labels = num_labels_task[task_name]
label_list = processor.get_labels()
tokenizer = BertTokenizer.from_pretrained(args.bert_model,
do_lower_case=args.do_lower_case)
train_examples = None
num_train_optimization_steps = None
if args.do_train:
train_examples = processor.get_train_examples(args.data_dir)
#print("train_examples :: ",len(list(train_examples)))
num_train_optimization_steps = int(
len(train_examples) / args.train_batch_size /
args.gradient_accumulation_steps) * args.num_train_epochs
if args.local_rank != -1:
num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size(
)
# Prepare model
cache_dir = args.cache_dir if args.cache_dir else os.path.join(
PYTORCH_PRETRAINED_BERT_CACHE, 'distributed_{}'.format(
args.local_rank))
#imodel = BertForSequenceClassification.from_pretrained(args.bert_model,
# cache_dir=cache_dir,
# num_labels = num_labels)
model = BertForTokenClassification.from_pretrained(args.bert_model,
cache_dir=cache_dir,
num_labels=num_labels)
if args.fp16:
model.half()
model.to(device)
if args.local_rank != -1:
try:
from apex.parallel import DistributedDataParallel as DDP
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training."
)
model = DDP(model)
elif n_gpu > 1:
model = torch.nn.DataParallel(model)
# Prepare optimizer
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{
'params':
[p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
'weight_decay':
0.01
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.0
}]
if args.fp16:
try:
from apex.optimizers import FP16_Optimizer
from apex.optimizers import FusedAdam
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training."
)
optimizer = FusedAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
bias_correction=False,
max_grad_norm=1.0)
if args.loss_scale == 0:
optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)
else:
optimizer = FP16_Optimizer(optimizer,
static_loss_scale=args.loss_scale)
else:
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=num_train_optimization_steps)
global_step = 0
nb_tr_steps = 0
tr_loss = 0
if args.do_train:
train_features = convert_examples_to_features(train_examples,
label_list,
args.max_seq_length,
tokenizer)
all_input_ids = torch.tensor([f.input_ids for f in train_features],
dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in train_features],
dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in train_features],
dtype=torch.long)
all_label_ids = torch.tensor([f.label_id for f in train_features],
dtype=torch.long)
train_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_label_ids)
if args.local_rank == -1:
train_sampler = RandomSampler(train_data)
else:
train_sampler = DistributedSampler(train_data)
train_dataloader = DataLoader(train_data,
sampler=train_sampler,
batch_size=args.train_batch_size)
model.train()
for _ in trange(int(args.num_train_epochs), desc="Epoch"):
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
for step, batch in enumerate(
tqdm(train_dataloader, desc="Iteration")):
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, label_ids = batch
#print(input_ids.shape,input_mask.shape,segment_ids.shape,label_ids.shape)
#print(input_ids[0])
#print(label_ids[0])
#logits = model(input_ids, segment_ids, input_mask)
#import pdb;pdb.set_trace()
#print(logits.view(-1, num_labels).shape, label_ids.view(-1).shape)
loss = model(input_ids, segment_ids, input_mask, label_ids)
if n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
optimizer.backward(loss)
else:
loss.backward()
# added clip
if args.clip is not None:
_ = torch.nn.utils.clip_grad_norm(model.parameters(),
args.clip)
tr_loss += loss.item()
nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1
if (step + 1) % args.gradient_accumulation_steps == 0:
if args.fp16:
# modify learning rate with special warm up BERT uses
# if args.fp16 is False, BertAdam is used that handles this automatically
lr_this_step = args.learning_rate * warmup_linear(
global_step / num_train_optimization_steps,
args.warmup_proportion)
for param_group in optimizer.param_groups:
param_group['lr'] = lr_this_step
optimizer.step()
optimizer.zero_grad()
global_step += 1
if args.do_train:
# Save a trained model and the associated configuration
model_to_save = model.module if hasattr(
model, 'module') else model # Only save the model it-self
output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)
torch.save(model_to_save.state_dict(), output_model_file)
output_config_file = os.path.join(args.output_dir, CONFIG_NAME)
with open(output_config_file, 'w') as f:
f.write(model_to_save.config.to_json_string())
# Load a trained model and config that you have fine-tuned
config = BertConfig(output_config_file)
#model = BertForSequenceClassification(config, num_labels=num_labels)
model = BertForTokenClassification(config, num_labels=num_labels)
model.load_state_dict(torch.load(output_model_file))
else:
#model = BertForSequenceClassification.from_pretrained(args.bert_model, num_labels=num_labels)
# Load a trained model and config that you have fine-tuned
print('for eval only......................')
output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)
output_config_file = os.path.join(args.output_dir, CONFIG_NAME)
config = BertConfig(output_config_file)
#model = BertForSequenceClassification(config, num_labels=num_labels)
model = BertForTokenClassification(config, num_labels=num_labels)
model.load_state_dict(torch.load(output_model_file))
model.to(device)
if args.do_eval and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
eval_examples = processor.get_dev_examples(args.data_dir)
#import pdb;pdb.set_trace()
#print("dev_eaxmples :: ",len(list(eval_examples)))
eval_features = convert_examples_to_features_pred(
eval_examples, label_list, args.max_seq_length, tokenizer)
all_input_ids = torch.tensor([f.input_ids for f in eval_features],
dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in eval_features],
dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in eval_features],
dtype=torch.long)
all_label_ids = torch.tensor([f.label_id for f in eval_features],
dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_label_ids)
# Run prediction for full data
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data,
sampler=eval_sampler,
batch_size=args.eval_batch_size)
model.eval()
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
predictions, true_labels = [], []
#predictions1 , true_labels1 = [], []
for input_ids, input_mask, segment_ids, label_ids in tqdm(
eval_dataloader, desc="Evaluating"):
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
label_ids = label_ids.to(device)
with torch.no_grad():
tmp_eval_loss = model(input_ids, segment_ids, input_mask,
label_ids)
logits = model(input_ids, segment_ids, input_mask)
logits = logits.detach().cpu().numpy()
label_ids = label_ids.to('cpu').numpy()
# get index till '[SEP]'
#print("label_list index SEP : ",label_list.index('[SEP]'))
pred_xx = [list(p) for p in np.argmax(logits, axis=2)]
pred_xx = [i[:i.index(label_list.index('[SEP]'))] for i in pred_xx]
label_ids_xx = [
i[:i.index(label_list.index('[SEP]'))]
for i in label_ids.tolist()
]
#print(label_ids_xx)
#print(pred_xx)
# new add
tmp_s = [
max(len(i), len(j)) for i, j in zip(label_ids_xx, pred_xx)
]
tmp_u = [(i + [31] * (k - len(i)) if len(i) != k else i,
j + [31] * (k - len(j)) if len(j) != k else j)
for i, j, k in zip(label_ids_xx, pred_xx, tmp_s)]
tmp_d1 = [h[0] for h in tmp_u]
tmp_d2 = [h[1] for h in tmp_u]
#print([list(p) for p in np.argmax(logits, axis=2)][:5])
#tmp_eval_accuracy = flat_accuracy(logits, label_ids)
tmp_eval_accuracy = flat_accc(pred_xx, label_ids_xx)
#tmp_eval_accuracy = flat_accc(tmp_d1, tmp_d2)
predictions.extend(tmp_d2)
true_labels.append(tmp_d1)
#predictions1.extend(pred_xx)
#true_labels1.append(label_ids_xx)
#print("tmp accuracy : ",tmp_eval_accuracy)
eval_loss += tmp_eval_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0)
nb_eval_steps += 1
eval_loss = eval_loss / nb_eval_steps
eval_accuracy = eval_accuracy / nb_eval_steps
loss = tr_loss / nb_tr_steps if args.do_train else None
pred_tags = [[label_list[p_i] if p_i != 31 else 'XXX' for p_i in p]
for p in predictions]
valid_tags = [[
label_list[l_ii] if l_ii != 31 else 'YYY' for l_ii in l_i
] for l in true_labels for l_i in l]
print("valid_tags : ", valid_tags[:10])
print("pred_tags : ", pred_tags[:10])
print("Validation F1-Score: {}".format(f1_score(valid_tags,
pred_tags)))
print("Validation accuracy_score : {}".format(
accuracy_score(valid_tags, pred_tags)))
print("Validation classification_report : {}".format(
classification_report(valid_tags, pred_tags)))
#print("X Validation F1-Score: {}".format(f1_score(true_labels1, predictions1)))
#print("X Validation accuracy_score : {}".format(accuracy_score(true_labels1, predictions1)))
#print("X Validation classification_report : {}".format(classification_report(true_labels1, predictions1)))
result = {
'eval_loss': eval_loss,
'eval_accuracy': eval_accuracy,
'global_step': global_step,
'loss': loss
}
print(result)
output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
with open(output_eval_file, "w") as writer:
for key in sorted(result.keys()):
writer.write("%s = %s\n" % (key, str(result[key])))
if args.do_test and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
eval_examples = processor.get_test_examples(args.data_dir)
#import pdb;pdb.set_trace()
eval_features = convert_examples_to_features_pred(
eval_examples, label_list, args.max_seq_length, tokenizer)
all_input_ids = torch.tensor([f.input_ids for f in eval_features],
dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in eval_features],
dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in eval_features],
dtype=torch.long)
all_label_ids = torch.tensor([f.label_id for f in eval_features],
dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_label_ids)
# Run prediction for full data
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data,
sampler=eval_sampler,
batch_size=args.eval_batch_size)
model.eval()
test_loss, test_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
predictions, true_labels = [], []
for input_ids, input_mask, segment_ids, label_ids in tqdm(
eval_dataloader, desc="Evaluating"):
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
label_ids = label_ids.to(device)
with torch.no_grad():
tmp_eval_loss = model(input_ids, segment_ids, input_mask,
label_ids)
logits = model(input_ids, segment_ids, input_mask)
logits = logits.detach().cpu().numpy()
label_ids = label_ids.to('cpu').numpy()
# get index till '[SEP]'
#print("label_list index SEP : ",label_list.index('[SEP]'))
pred_xx = [list(p) for p in np.argmax(logits, axis=2)]
pred_xx = [i[:i.index(label_list.index('[SEP]'))] for i in pred_xx]
label_ids_xx = [
i[:i.index(label_list.index('[SEP]'))]
for i in label_ids.tolist()
]
#print(label_ids_xx)
#print(pred_xx)
# new add
tmp_s = [
max(len(i), len(j)) for i, j in zip(label_ids_xx, pred_xx)
]
tmp_u = [(i + [31] * (k - len(i)) if len(i) != k else i,
j + [31] * (k - len(j)) if len(j) != k else j)
for i, j, k in zip(label_ids_xx, pred_xx, tmp_s)]
tmp_d1 = [h[0] for h in tmp_u]
tmp_d2 = [h[1] for h in tmp_u]
#print([list(p) for p in np.argmax(logits, axis=2)][:5])
#tmp_eval_accuracy = flat_accuracy(logits, label_ids)
tmp_eval_accuracy = flat_accc(pred_xx, label_ids_xx)
#tmp_eval_accuracy = flat_accc(tmp_d1, tmp_d2)
predictions.extend(tmp_d2)
true_labels.append(tmp_d1)
#print("tmp accuracy : ",tmp_eval_accuracy)
test_loss += tmp_eval_loss.mean().item()
test_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0)
nb_eval_steps += 1
test_loss = test_loss / nb_eval_steps
test_accuracy = test_accuracy / nb_eval_steps
loss = tr_loss / nb_tr_steps if args.do_train else None
pred_tags = [[label_list[p_i] if p_i != 31 else 'XXX' for p_i in p]
for p in predictions]
valid_tags = [[
label_list[l_ii] if l_ii != 31 else 'YYY' for l_ii in l_i
] for l in true_labels for l_i in l]
print("valid_tags : ", valid_tags[:10])
print("pred_tags : ", pred_tags[:10])
print("Test F1-Score: {}".format(f1_score(valid_tags, pred_tags)))
print("Test accuracy_score : {}".format(
accuracy_score(valid_tags, pred_tags)))
print("Test classification_report : {}".format(
classification_report(valid_tags, pred_tags)))
#print("X Test F1-Score: {}".format(f1_score(true_labels, predictions)))
#print("X Test accuracy_score : {}".format(accuracy_score(true_labels, predictions)))
#print("X Test classification_report : {}".format(classification_report(true_labels, predictions)))
result = {
'test_loss': test_loss,
'test_accuracy': test_accuracy,
'global_step': global_step,
'loss': loss
}
print(result)
output_test_file = os.path.join(args.output_dir, "test_results.txt")
with open(output_test_file, "w") as writer:
for key in sorted(result.keys()):
writer.write("%s = %s\n" % (key, str(result[key])))
if args.do_pred and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
#eval_examples = processor.get_dev_examples(args.data_dir)
model.eval()
while True:
print(
'Enter a text to get NER. otherwise press Ctrl+C to close session.'
)
text_a = input('>>>')
#"Japan began the defence of their Asian Cup title with a lucky 2-1 win against Syria in a Group C championship match on Friday . ."
eval_examples = {
'text_a': text_a,
'text_b':
"The foodservice pie business does not fit our long-term growth strategy .",
'label': '1',
'guid': '12345'
}
eval_features = convert_examples_to_features_test(
eval_examples, label_list, args.max_seq_length, tokenizer)
all_input_ids = torch.tensor([f.input_ids for f in eval_features],
dtype=torch.long)
all_input_mask = torch.tensor(
[f.input_mask for f in eval_features], dtype=torch.long)
all_segment_ids = torch.tensor(
[f.segment_ids for f in eval_features], dtype=torch.long)
all_label_ids = torch.tensor([f.label_id for f in eval_features],
dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_label_ids)
# Run prediction for full data
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data,
sampler=eval_sampler,
batch_size=args.eval_batch_size)
#model.eval()
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
predictions, true_labels = [], []
for input_ids, input_mask, segment_ids, label_ids in tqdm(
eval_dataloader, desc="Evaluating"):
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
label_ids = label_ids.to(device)
with torch.no_grad():
tmp_eval_loss = model(input_ids, segment_ids, input_mask,
label_ids)
logits = model(input_ids, segment_ids, input_mask)
logits = logits.detach().cpu().numpy()
label_ids = label_ids.to('cpu').numpy()
pred_xx = [list(p) for p in np.argmax(logits, axis=2)]
pred_xx = [
i[:i.index(label_list.index('[SEP]'))] for i in pred_xx
]
print(pred_xx)
print([[label_list[p_i] if p_i != 31 else 'XXX' for p_i in p]
for p in pred_xx])
