Unveiling the Thrill: Tennis W15 Gurugram India
Step into the electrifying world of Tennis W15 Gurugram India, where every serve, volley, and match is a spectacle of skill and strategy. This prestigious event draws in tennis enthusiasts from across the globe, eager to witness the rise of new champions and the prowess of seasoned players. Whether you're a die-hard fan or a casual observer, the daily matches provide an endless source of excitement and entertainment.
Match Updates: Stay Informed Every Day
With matches being updated daily, you're never left out of the action. Our platform ensures you receive real-time updates, allowing you to follow your favorite players' journey through the tournament. From thrilling victories to unexpected upsets, every match is a story waiting to unfold.
- Live Scores: Get instant updates on scores as they happen.
- Match Highlights: Watch key moments from each game.
- Player Stats: Keep track of player performances and statistics.
Betting Predictions: Expert Insights for Your Wager
Betting on tennis can be an exhilarating experience, especially when armed with expert predictions. Our team of seasoned analysts provides insights and forecasts to help you make informed bets. Whether you're new to betting or a seasoned pro, these predictions aim to enhance your betting strategy and increase your chances of success.
- Daily Predictions: Receive expert betting tips for each match.
- Analytical Reports: Dive deep into match analyses and player form.
- Betting Strategies: Learn how to optimize your betting approach.
The Tournament at a Glance
Tennis W15 Gurugram India is more than just a tournament; it's a celebration of tennis excellence. Held in the vibrant city of Gurugram, this event showcases the best talent in women's tennis. The tournament features both established stars and emerging talents, making it a must-watch for anyone passionate about the sport.
- Dates and Schedule: Check out the full tournament schedule to plan your viewing.
- Venue Details: Discover more about the state-of-the-art facilities hosting the matches.
- Past Winners: Learn about previous champions and their remarkable journeys.
Expert Betting Tips: Maximizing Your Odds
Betting on tennis requires not just luck but also knowledge and strategy. Our expert tips are designed to guide you through the complexities of tennis betting, helping you make smarter decisions and potentially increasing your winnings.
- Understanding Odds: Learn how to interpret betting odds effectively.
- Making Informed Bets: Use expert analysis to guide your betting choices.
- Risk Management: Strategies for managing your bankroll wisely.
Favorite Players to Watch
Every tournament brings its share of standout performances. Here are some players to keep an eye on during Tennis W15 Gurugram India:
- Jane Doe: Known for her powerful serves and aggressive play style.
- Alice Smith: A rising star with exceptional baseline skills.
- Maria Gonzalez: Renowned for her strategic gameplay and mental toughness.
Daily Match Highlights: What You Missed Today
If you couldn't catch the live action, don't worry! Our daily highlights recap ensures you never miss out on the excitement. From breathtaking rallies to decisive volleys, relive the best moments from each day's matches.
- Serve Breakdowns: Analyze key serves that turned the tide of matches.
- Critical Points: Watch crucial points that defined each game's outcome.
- Injury Updates: Stay informed about player injuries and recovery statuses.
Tennis W15 Gurugram India: A Cultural Phenomenon
The tournament is more than just a sporting event; it's a cultural phenomenon that brings together fans from diverse backgrounds. The atmosphere in Gurugram is electric, with fans cheering passionately for their favorite players. The event also highlights local culture, offering visitors a taste of Indian hospitality and traditions.
- Cultural Events: Enjoy cultural performances and local cuisine during match breaks.
- Fan Engagement Activities: Participate in interactive sessions with players and coaches.
- Social Media Buzz: Follow #TennisW15Gurugram for real-time updates and fan interactions.
Betting Strategies: Tips for Every Level
<|repo_name|>YaoZhouqiang/BiDaL-NER<|file_sep|>/data_loader.py
import numpy as np
import os
import random
class DataLoader(object):
def __init__(self,data_path,batch_size,max_len,max_entity_length=20):
self.batch_size = batch_size
self.max_len = max_len
self.data_path = data_path
self.max_entity_length = max_entity_length
def get_batch_data(self):
f_list = os.listdir(self.data_path)
while True:
random.shuffle(f_list)
for f_name in f_list:
f_path = os.path.join(self.data_path,f_name)
with open(f_path,'r') as fr:
ques_id,sen_num = fr.readline().strip().split()
sen_num = int(sen_num)
ques = fr.readline().strip()
all_sen = []
for i in range(sen_num):
sen = fr.readline().strip()
all_sen.append(sen)
batch_data,batch_label,batch_mask,batch_pos1,batch_pos2,batch_seq_len,batch_sen_len,batch_sen_num,batch_ques_len,batch_ques_id = self.convert_single_example(ques,all_sen)
for i in range(0,len(batch_data),self.batch_size):
yield batch_data[i:i+self.batch_size],batch_label[i:i+self.batch_size],batch_mask[i:i+self.batch_size],batch_pos1[i:i+self.batch_size],batch_pos2[i:i+self.batch_size],batch_seq_len[i:i+self.batch_size],batch_sen_len[i:i+self.batch_size],batch_sen_num[i:i+self.batch_size],batch_ques_len[i:i+self.batch_size],batch_ques_id[i:i+self.batch_size]
def convert_single_example(self,ques,all_sen):
sample_idx = [0]
for sen in all_sen:
sample_idx.append(sample_idx[-1] + len(sen))
batch_data = []
batch_label = []
batch_mask = []
batch_pos1 = []
batch_pos2 = []
batch_seq_len = []
batch_sen_len = []
batch_sen_num = []
batch_ques_len = []
batch_ques_id = []
for i,sample_idx_i in enumerate(sample_idx[:-1]):
sample_idx_j = sample_idx[i+1]
sample_data,sample_label,sample_mask,sample_pos1,sample_pos2,sample_seq_len,sample_sen_len,sample_ques_len,sample_ques_id= self.convert_single_sample(ques[0:sample_idx_j-sample_idx_i],sample_idx_i)
for j in range(len(sample_data)):
if sample_seq_len[j] > self.max_len:
continue
batch_data.append(sample_data[j])
batch_label.append(sample_label[j])
batch_mask.append(sample_mask[j])
batch_pos1.append(sample_pos1[j])
batch_pos2.append(sample_pos2[j])
batch_seq_len.append(sample_seq_len[j])
batch_sen_len.append(sample_sen_len[j])
batch_sen_num.append(i)
batch_ques_len.append(sample_ques_len[j])
batch_ques_id.append(sample_ques_id[j])
return batch_data,batch_label,batch_mask,batch_pos1,batch_pos2,batch_seq_len,batch_sen_len,batch_sen_num,batch_ques_len,batch_ques_id
def convert_single_sample(self,sentence,start_idx):
word_list,pos_list,label_list,single_sentence_mask,single_sentence_startpos,single_sentence_endpos,single_sentence_seq_lens,single_sentence_entity_lens= self.seg_single_sentence(sentence,start_idx)
data,label,pos1,pos2,mask,seq_lens,sentence_lens=single_sentence_to_batch(word_list,pos_list,label_list,
single_sentence_mask,
single_sentence_startpos,
single_sentence_endpos,
single_sentence_seq_lens,
single_sentence_entity_lens,
self.max_entity_length)
return data,label,mask,pos1,pos2,seq_lens,sentence_lens,len(word_list),start_idx
def seg_single_sentence(self,sentence,start_idx):
word_list=[]
pos_list=[]
label_list=[]
entity_masks=[]
start_positions=[]
end_positions=[]
seq_lens=[]
data= sentence.strip().split(' ')
label= ['O']*len(data)
pos= ['_']*len(data)
for i,data_i in enumerate(data):
if '<' in data_i:
try:
label[i]= data_i.split('')[-1]
data_i= data_i.split('')[1]
data_i= data_i.split('>')[-1]
if '(' in data_i:
try:
pos[i]= data_i.split(')')[0].split('(')[1]
data_i= data_i.split(')')[1]
except:
pos[i]= data_i.split('(')[0].split(')')[1]
data_i= data_i.split(')')[-1]
if '[' in data_i:
try:
pos[i]= data_i.split(']')[0].split('[')[1]
data_i= data_i.split(']')[1]
except:
pos[i]= data_i.split('[')[0].split(']')[1]
data_i= data_i.split(']')[-1]
word_list.append(data_i)
pos_list.append(pos[i])
label_list.append(label[i])
seq_lens.append(len(data))
if 'B-' in label[0]:
entity_masks.append([True]*len(data))
start_positions.append([i for i,l in enumerate(label) if l == 'B-' + label[0][2:]]+[len(data)])
end_positions.append([i for i,l in enumerate(label) if l == 'E-' + label[0][2:] or l == 'S-' + label[0][2:]]+[len(data)])
else:
entity_masks.append([False]*len(data))
start_positions.append([len(data)])
end_positions.append([len(data)])
word_list=[word_list[:self.max_len]]
pos_list=[pos_list[:self.max_len]]
label_list=[label_list[:self.max_len]]
entity_masks=[entity_masks[:self.max_len]]
start_positions=[start_positions[:self.max_len]]
end_positions=[end_positions[:self.max_len]]
seq_lens=[seq_lens[:self.max_len]]
single_sentence_entity_lens+= [min(len(start_positions[0]),len(end_positions[0]))]
start_positions+= [[min(i,len(word_list[0])-1) for i in start_position]+[len(word_list[0])] for start_position in start_positions]
end_positions+= [[min(i,len(word_list[0])-1) for i in end_position]+[len(word_list[0])] for end_position in end_positions]
for i,end_position in enumerate(end_positions):
if end_position[0]== len(word_list[0]):
end_positions[i][0]= start_positions[i][0]
entity_masks+= [[True]*min(len(start_positions[0]),len(end_positions[0]))+[False]*(len(word_list[0])-min(len(start_positions[0]),len(end_positions[0]))) for entity_mask in entity_masks]
word_lists+= [word_list]
pos_lists+= [pos_list]
label_lists+= [label_list]
single_sentence_mask+= [entity_mask+ [False]*(self.max_entity_length-len(entity_mask))]
single_sentence_startpos+= [[start_position+start_idx+sentence_count*10+10000000000]+[10000000000]*(self.max_entity_length-len(start_position)-1) for start_position in start_positions]
single_sentence_endpos+= [[end_position+start_idx+sentence_count*10+10000000000]+[10000000000]*(self.max_entity_length-len(end_position)-1) for end_position in end_positions]
else:
word_lists+= [[]]
pos_lists+= [[]]
label_lists+= [[]]
single_sentence_mask+= [[]]
single_sentence_startpos+= [[]]
single_sentence_endpos+= [[]]
single_sentence_entity_lens+= [0]
sentence_count += len(sentence)
else:
word_lists+= [[]]
pos_lists+= [[]]
label_lists+= [[]]
single_sentence_mask+= [[]]
single_sentence_startpos+= [[]]
single_sentence_endpos+= [[]]
single_sentence_entity_lens+= [0]
return word_lists,pos_lists,label_lists,single_sentence_mask,single_sentence_startpos,single_sentence_endpos,single_sequence_lengths,single_sentence_entity_lens
def single_sample_to_batch(word,pos,label,
mask,
start_position,
end_position,
seq_length,
entity_length):
max_seq_length= max(seq_length)
max_entity_length= max(entity_length)
max_word_length= max([len(w)for w in word])
word=np.zeros((max_seq_length,max_word_length),dtype='int32')
pos=np.zeros((max_seq_length),dtype='int32')
label=np.zeros((max_seq_length),dtype='int32')
mask=np.zeros((max_seq_length,max_entity_length),dtype='int32')
start_position=np.zeros((max_seq_length,max_entity_length),dtype='int32')
end_position=np.zeros((max_seq_length,max_entity_length),dtype='int32')
seq_lengths=np.array(seq_length,dtype='int32')
entity_lengths=np.array(entity_length,dtype='int32')
for i,(w,p,l,m,st,en)in enumerate(zip(word,pos,label,mask,start_position,end_position)):
word_[np.arange(min(len(w),max_word_length)),np.arange(min(len(w),max_word_length))]= w[:min(len(w),max_word_length)]
pos_[i,:min(len(p),max_seq_length)]= p[:min(len(p),max_seq_length)]
label_[i,:min(len(l),max_seq_length)]= l[:min(len(l),max_seq_length)]
mask_[i,:min(len(m),max_entity_length)]= m[:min(len(m),max_entity_length)]
start_position_[i,:min(len(st),max_entity_length)]= st[:min(len(st),max_entity_length)]
end_position_[i,:min(len(en),max_entity_length)]= en[:min(len(en),max_entity_length)]
return word_,label_,mask_,start_position_,end_position_,seq_lengths,entity_lengths
def single_sentences_to_batch(word_lists,pos_lists,label_lists,
single_sentences_mask,
single_sentences_startpos,
single_sentences_endpos,
single_sequence_lengths,
single_sentences_entity_lengths):
max_sequence_lengths= max(single_sequence_lengths)
max_sentences_number= len(single_sequence_lengths)
max_sentences_word_lengths= max([max([len(w)for w in w_l])for w_l in word_lists])
max_sentences_word_number= max([sum([l