Exploring Tennis W75 Kursumlijska Banja Serbia: A Comprehensive Guide
Welcome to the vibrant world of tennis in Kursumlijska Banja, Serbia! Our platform is dedicated to providing you with the freshest matches and expert betting predictions for the W75 category. Whether you're a seasoned player or a casual fan, we've got you covered with detailed insights and engaging content. Let's dive into the exciting world of tennis in this picturesque location.
Why Kursumlijska Banja?
Kursumlijska Banja, nestled in the heart of Serbia, is a haven for tennis enthusiasts. Known for its stunning natural beauty and serene environment, it offers an ideal backdrop for both competitive matches and leisurely play. The town's commitment to sports and wellness makes it a perfect spot for hosting the W75 tennis category.
Understanding the W75 Category
The W75 category in tennis is specifically designed for women aged 75 and above. This category celebrates the enduring spirit and passion for the game among senior players. It provides an opportunity for these athletes to showcase their skills, experience, and love for tennis on an international stage.
- Inclusivity: The W75 category promotes inclusivity by allowing senior women to participate actively in competitive tennis.
- Experience: Players bring years of experience and strategic play to the court, making matches both challenging and entertaining.
- Community: It fosters a sense of community among senior players, encouraging camaraderie and mutual respect.
Latest Matches and Updates
Our platform ensures you never miss a beat with daily updates on fresh matches. Whether you're following a favorite player or exploring new talents, we provide comprehensive coverage of every match. Here's what you can expect:
- Real-Time Updates: Get live scores and match progress as it happens.
- Detailed Analysis: In-depth reviews of each match, highlighting key moments and player performances.
- Expert Commentary: Insights from seasoned analysts who bring a deeper understanding of the game.
Betting Predictions: Expert Insights
Betting on tennis can be both thrilling and rewarding. Our expert predictions are designed to help you make informed decisions. Here's how we approach betting predictions:
- Data-Driven Analysis: We use historical data and statistical models to predict match outcomes.
- Player Form: Assessing current form, recent performances, and head-to-head records.
- Tournament Conditions: Considering factors like court surface, weather conditions, and location-specific challenges.
Tips for Successful Betting
To enhance your betting experience, consider these tips:
- Diversify Bets: Spread your bets across different matches to manage risk.
- Stay Informed: Keep up with the latest news and updates about players and tournaments.
- Analyze Trends: Look for patterns in player performances and betting odds.
Famous Players in the W75 Category
The W75 category boasts some remarkable players who have made significant contributions to tennis. Here are a few notable names:
- Jane Doe: Known for her exceptional serve and strategic gameplay, Jane has been a dominant force in the W75 category.
- Mary Smith: With numerous titles under her belt, Mary's resilience and skill continue to inspire many.
- Linda Johnson: A veteran player with decades of experience, Linda brings a wealth of knowledge to every match.
Tournaments in Kursumlijska Banja
Kursumlijska Banja hosts several prestigious tournaments that attract top talent from around the world. Here are some highlights:
- Kursumlijska Open: A major tournament featuring players from various countries competing in intense matches.
- Banja Classic Series: A series of friendly matches that emphasize sportsmanship and community engagement.
- Serbian Senior Championship: The pinnacle of senior tennis in Serbia, showcasing the best players in the country.
The Role of Technology in Tennis
Technology plays a crucial role in enhancing the tennis experience. From advanced analytics to innovative training tools, here's how technology is shaping the game:
- Data Analytics: Teams use data analytics to develop strategies and improve player performance.
- Sports Science: Advances in sports science help players optimize their training regimes and recovery processes.
- Virtual Reality (VR): VR technology is used for simulating match scenarios and improving decision-making skills.
Sustainability in Tennis
Sustainability is becoming increasingly important in sports. Tennis organizations are adopting eco-friendly practices to minimize their environmental impact. Some initiatives include:
- Eco-Friendly Courts: Using sustainable materials for constructing tennis courts.
- Waste Management Programs: Implementing recycling programs at tournaments to reduce waste.
- Eco-Conscious Events: Organizing events that promote environmental awareness among players and fans.
Culture and Community Engagement
Tennis is more than just a sport; it's a cultural phenomenon that brings people together. In Kursumlijska Banja, community engagement is at the heart of tennis activities. Here are some ways this is achieved:
- Youth Programs: Initiatives aimed at introducing young people to tennis and nurturing future talent.
- Cultural Events: Hosting cultural events alongside tournaments to celebrate local heritage and traditions.
- Volunteer Opportunities: Encouraging community members to volunteer at tournaments, fostering a sense of ownership and pride.
The Future of Tennis in Kursumlijska Banja
The future looks bright for tennis in Kursumlijska Banja. With ongoing investments in infrastructure and community programs, the town is poised to become a major hub for tennis enthusiasts worldwide. Here are some exciting developments on the horizon:
- New Facilities: Plans for state-of-the-art training centers and stadiums are underway.
shubhamrathod05/CS551-Project<|file_sep|>/src/README.md
# Code
Contains all code used for project
## Data Preprocessing
- data_preprocessing.py: Loads raw data from [dataset](https://github.com/yenchenlin/ChineseWordEmbedding), runs preprocessing on it (removes punctuation & numbers) using regexes.
- Preprocessed data stored as [preprocessed_data.json](https://drive.google.com/file/d/1yQfZmS5YU8QjJGQXU6nOxKoqL4NtmV7a/view?usp=sharing)
## Evaluation
- evaluate_model.py: Evaluates trained model on validation set.
## Model Training
- train.py: Trains word embedding model using skipgram (SGNS) architecture on preprocessed data.
- Saves trained model as [model.pt](https://drive.google.com/file/d/1lFVY22iMOCcKwT4xWZZQgZfQvQbXg9Rw/view?usp=sharing)
## Similarity Tests
- test_similarity.py: Performs similarity tests on trained model.
<|file_sep|># CS 551 Project
This repository contains code used during our project for CS 551 (Natural Language Processing) at UC San Diego.
## Project Description
### Problem Statement
We aim at training word embeddings that can capture semantic relationships between words based on distributional similarity.
### Dataset
We use [Chinese Word Embedding dataset](https://github.com/yenchenlin/ChineseWordEmbedding) which contains Chinese text corpus extracted from Wikipedia articles.
### Approach
We train word embeddings using skipgram (SGNS) architecture.
### Evaluation Metrics
We evaluate our model using cosine similarity between word vectors:
1. Word Similarity Test: We compare cosine similarities between word vectors with human ratings.
2. Analogy Test: We test if vector operations such as `king - man + woman = queen` hold true.
## Running Code
### Data Preprocessing
python data_preprocessing.py
### Model Training
python train.py --embeddingsize 300 --learningrate 0.01 --batchsize 128 --epochs 5 --window 5 --negsamples 15 --outputfilename "model.pt"
### Evaluation
python evaluate_model.py --embeddingsize 300 --window 5 --negsamples 15 --outputfilename "model.pt" --numwords 50000 --batchsize 128
### Similarity Tests
python test_similarity.py --embeddingsize 300 --window 5 --negsamples 15 --outputfilename "model.pt" --numwords 50000
## Results
<|repo_name|>shubhamrathov06/CS551-Project<|file_sep|>/src/data_preprocessing.py
import re
import json
# Load raw data
with open("data/chinese_3000_2.txt", "r") as file:
raw_data = file.read()
# Remove punctuation & numbers using regex
data = re.sub(r"[^a-zA-Zu4e00-u9fa5]", " ", raw_data)
data = re.sub(r"s+", " ", data)
# Save preprocessed data
with open("preprocessed_data.json", "w") as file:
json.dump(data.split(), file)<|repo_name|>shubhamrathov06/CS551-Project<|file_sep|>/src/train.py
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import numpy as np
import argparse
import json
from tqdm import tqdm
class WordEmbeddingDataset(Dataset):
def __init__(self):
self.data = []
self.word2idx = {}
self.idx2word = {}
with open("preprocessed_data.json", "r") as file:
data = json.load(file)
for i in range(len(data)):
if i == len(data) - 1:
break
if not self.word2idx.get(data[i]):
self.word2idx[data[i]] = len(self.word2idx)
self.idx2word[len(self.idx2word)] = data[i]
if not self.word2idx.get(data[i+1]):
self.word2idx[data[i+1]] = len(self.word2idx)
self.idx2word[len(self.idx2word)] = data[i+1]
context_words = []
for j in range(i-window+1,i+window+1):
if j == i:
continue
if j == -1 or j >= len(data):
continue
if not self.word2idx.get(data[j]):
self.word2idx[data[j]] = len(self.word2idx)
self.idx2word[len(self.idx2word)] = data[j]
context_words.append(self.word2idx[data[j]])
context_words = list(set(context_words))
for context_word in context_words:
self.data.append([self.word2idx[data[i]], context_word])
def __len__(self):
return len(self.data)
def __getitem__(self, index):
return self.data[index]
class SGNS(nn.Module):
def __init__(self, vocab_size, embedding_size):
super(SGNS, self).__init__()
self.input_embeddings = nn.Embedding(vocab_size+1, embedding_size)
self.output_embeddings = nn.Embedding(vocab_size+1, embedding_size)
def forward(self, inputs):
input_vectors = self.input_embeddings(inputs[0])
output_vectors = self.output_embeddings(inputs[1])
return torch.bmm(input_vectors.unsqueeze(1), output_vectors.unsqueeze(2)).squeeze()
def get_negatives(target_indices):
negatives_list = []
for target_index in target_indices:
negative_indices = np.random.choice(np.arange(0,vocab_size), size=neg_samples)
negatives_list.append(negative_indices)
return negatives_list
def train(model, train_loader):
model.train()
for epoch in range(args.epochs):
for inputs,target_indices,negatives_list,batch_size,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s] in enumerate(zip(train_loader)):
inputs,target_indices,negatives_list=batch_size,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s
target_vectors=model(input_vectors)
negative_vectors=model(negative_indices)
log_prob=torch.sum(torch.log(torch.sigmoid(target_vectors)))+torch.sum(torch.log(torch.sigmoid(-negative_vectors)))
loss=-log_prob/batch_size
optimizer.zero_grad()
loss.backward()
optimizer.step()
def save_model(model):
torch.save(model.state_dict(), args.outputfilename)
if __name__ == "__main__":
parser=argparse.ArgumentParser(description="Train Word Embedding Model")
parser.add_argument("--embeddingsize", type=int,default=100)
parser.add_argument("--learningrate", type=float,default=0.01)
parser.add_argument("--batchsize", type=int,default=64)
parser.add_argument("--epochs", type=int,default=5)
parser.add_argument("--window", type=int,default=5)
parser.add_argument("--negsamples", type=int,default=15)
parser.add_argument("--outputfilename", default="model.pt")
args=parser.parse_args()
vocab_size=len(dataset.word2idx)
model=SGNS(vocab_size,args.embeddingsize).to(device)
criterion=nn.BCEWithLogitsLoss()
optimizer=optim.Adam(model.parameters(),lr=args.learningrate)
dataset=WordEmbeddingDataset()
data_loader=DataLoader(dataset,args.batchsize,num_workers=4)
for epoch in range(args.epochs):
print("Epoch:",epoch+1,"of",args.epochs)
total_loss=0
total_batches=len(dataset)//args.batchsize
for inputs,target_indices,negatives_list=batch_size,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s]in enumerate(zip(train_loader)):
inputs,target_indices=negatives_list=batch_size,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s
input_vectors=torch.tensor(inputs[0]).to(device)
target_vectors=torch.tensor(target_indices).to(device)
negative_indices=torch.tensor(negatives_list).to(device)
target_vectors=model(input_vectors)
negative_vectors=model(negative_indices)
log_prob=torch.sum(torch.log(torch.sigmoid(target_vectors)))+torch.sum(torch.log(torch.sigmoid(-negative_vectors)))
loss=-log_prob/batch_size
total_loss+=loss.item()
print("Batch:",batch_index+1,"of",total_batches,"Loss:",loss.item())
torch.cuda.empty_cache()
print("Average Loss:",total_loss/total_batches)
save_model(model)<|repo_name|>shubhamrathov06/CS551-Project<|file_sep|>/src/test_similarity.py
import torch
import numpy as np
import argparse
import json
from tqdm import tqdm
device='cuda' if torch.cuda.is_available()