1. Division stats & predictions

What is the Cyprus 1st Division?

The Cyprus 1st Division is a professional football league in Cyprus that serves as the second tier of Cypriot football. It consists of 14 teams competing for promotion to the top division and avoiding relegation.

How can I follow daily match updates?

You can follow daily match updates through social media platforms, fan forums, and dedicated sports news websites that cover Cypriot football extensively.

What makes betting predictions valuable?

Betting predictions offer analytical insights based on various factors such as team form, head-to-head records, injuries, and weather conditions. These insights help bettors make informed decisions and enhance their engagement with the sport.

Are there any live streaming options for matches?

Yes, many matches are available for live streaming through official sports channels or online platforms that specialize in broadcasting Cypriot football games.

Creative Ways Clubs Engage with Communities

• Youth Academies: Developing young talent through training programs that emphasize skill development and sportsmanship.
• Social Responsibility Initiatives: Organizing charity events and supporting local causes to give back to the community.
• Fan Engagement Activities: Hosting meet-and-greet sessions with players and organizing fan days at stadiums to foster closer connections with supporters.

Innovative Technologies Shaping Football Engagement

• Multimedia Apps: Comprehensive apps offering live scores, player stats, video highlights, and interactive features keep fans engaged regardless of location.
• Social Media Integration: Platforms like Instagram Live or TikTok allow fans to share their experiences instantly with global audiences.COSAL-RUL/wordnet-sense-disambiguation<|file_sep|>/README.md # Wordnet Sense Disambiguation This repository contains code examples used in my research paper titled [A comparative study on word sense disambiguation using WordNet](https://cosal.rul.ac.bd/papers/2020/COSAL_2020_CSD_WSD.pdf). ## Introduction The task is one of most fundamental problems in Natural Language Processing (NLP). In this paper we have tried some popular WSD algorithms by comparing them using precision recall metrics. ## Requirements * Python >= 3 * Scikit-learn >= 0.21 ## Dataset The dataset used in this research paper can be found [here](https://drive.google.com/file/d/1yUjGJ6LzgIwVwKkF9HnUv5G5cOyqHzsJ/view?usp=sharing). ## Getting Started ### Installation bash pip install -r requirements.txt ### Training Models To train all models: bash python train.py --data_path ./data/data.csv --train_size 0.8 --output_path ./models/ ### Evaluating Models To evaluate all models: bash python evaluate.py --data_path ./data/data.csv --test_size 0.2 --models_dir ./models/ ### Usage You can also train or evaluate specific models by specifying `--model` argument. bash # Train baseline model. python train.py --model baseline --data_path ./data/data.csv --train_size 0.8 --output_path ./models/ # Evaluate baseline model. python evaluate.py --model baseline --data_path ./data/data.csv --test_size 0.2 --models_dir ./models/ ## Results | Model | Precision | Recall | F1-score | |-------|-----------|--------|----------| | Baseline | 53% | 53% | 53% | | Lesk | 52% | 52% | 52% | | Yarowsky | 50% | 50% | 50% | | Simple NN | 52% | 52% | 52% | | CNN-LSTM | **65%** | **65%** | **65%** | ## License [MIT](https://choosealicense.com/licenses/mit/) <|repo_name|>COSAL-RUL/wordnet-sense-disambiguation<|file_sep|>/utils.py import re import os from nltk.corpus import wordnet as wn def get_words_from_sentence(sentence): return re.findall(r'w+', sentence.lower()) def get_word_senses(word): return wn.synsets(word) def get_words_from_senses(senses): return [sense.name()[:-2] for sense in senses] def get_word_embedding(word): return wn.synset(word).definition().split() def save_model(model_name: str, model, output_path: str = 'models'): if not os.path.exists(output_path): os.makedirs(output_path) model.save(f'{output_path}/{model_name}.joblib') def load_model(model_name: str, output_path: str = 'models'): import joblib return joblib.load(f'{output_path}/{model_name}.joblib') def get_pos_from_wordnet(pos): if pos == 'a': return 'a' elif pos == 'n': return 'n' elif pos == 'v': return 'v' elif pos == 'r': return 'r' else: raise ValueError('Invalid POS tag') <|file_sep|># coding=utf-8 import pandas as pd from sklearn.model_selection import train_test_split from utils import get_words_from_sentence def read_data(data_file): df = pd.read_csv(data_file) df['sentence'] = df['sentence'].apply(get_words_from_sentence) return df if __name__ == '__main__': import argparse parser = argparse.ArgumentParser() parser.add_argument('--data_path', type=str, default='./data/data.csv', help='Path where data file stored.') parser.add_argument('--train_size', type=float, default=0.8, help='Train data size.') parser.add_argument('--test_size', type=float, default=0.2, help='Test data size.') args = parser.parse_args() df = read_data(args.data_path) train_df = df.sample(frac=args.train_size) test_df = df.drop(train_df.index) train_df.to_csv('train.csv', index=False) test_df.to_csv('test.csv', index=False) <|file_sep|># coding=utf-8 import os import pandas as pd from sklearn.metrics import classification_report from utils import load_model def evaluate_models(models_dir: str = 'models', data_file: str = './data/test.csv'): data = pd.read_csv(data_file) predictions = {} for model_file in os.listdir(models_dir): model_name = model_file.split('.')[0] model = load_model(model_name=model_name, output_path=models_dir) predictions[model_name] = model.predict(data['sentence']) print(classification_report(data['target'], predictions)) if __name__ == '__main__': import argparse parser = argparse.ArgumentParser() parser.add_argument('--data_path', type=str, default='./data/test.csv', help='Path where test file stored.') parser.add_argument('--models_dir', type=str, default='models', help='Path where trained models stored.') args = parser.parse_args() evaluate_models(data_file=args.data_path, models_dir=args.models_dir) <|file_sep|># coding=utf-8 from keras.models import Sequential from keras.layers import Dense from keras.layers.embeddings import Embedding from keras.preprocessing.sequence import pad_sequences class SimpleNN: def __init__(self): self.model = Sequential() self.model.add(Embedding(10000, input_length=50)) self.model.add(Dense(32)) self.model.add(Dense(1)) def fit(self, X_train, y_train, batch_size=32, epochs=10): X_train_padded = pad_sequences(X_train, padding='post') self.model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy']) self.model.fit(X_train_padded, y_train, batch_size=batch_size, epochs=epochs) def predict(self, X_test): X_test_padded = pad_sequences(X_test, padding='post') return self.model.predict_classes(X_test_padded) def save(self, model_name: str, output_path: str = 'models'): from utils import save_model save_model(model_name=model_name + '_nn', model=self.model, output_path=output_path) if __name__ == '__main__': import argparse parser = argparse.ArgumentParser() parser.add_argument('--model', type=str, default='simple_nn', help='Model name.') parser.add_argument('--data_path', type=str, default='./data/train.csv', help='Path where train file stored.') parser.add_argument('--output_path', type=str, default='./models/', help='Path where trained model saved.') args = parser.parse_args() from utils import get_words_from_sentence data = pd.read_csv(args.data_path) X_train = data['sentence'].apply(get_words_from_sentence).values.tolist() y_train = data['target'].values.tolist() model = SimpleNN() model.fit(X_train=X_train, y_train=y_train) model.save(model_name=args.model + '_nn', output_path=args.output_path) <|repo_name|>COSAL-RUL/wordnet-sense-disambiguation<|file_sep|>/baseline.py # coding=utf-8 class Baseline: def fit(self): pass def predict(self): pass def save(self): pass if __name__ == '__main__': import argparse parser = argparse.ArgumentParser() parser.add_argument('--model', type=str, default='baseline', help='Model name.') parser.add_argument('--data_path', type=str, default='./data/train.csv', help='Path where train file stored.') parser.add_argument('--output_path', type=str, default='./models/', help='Path where trained model saved.') args = parser.parse_args() data = pd.read_csv(args.data_path) model = Baseline() model.fit() model.save(model_name=args.model + '_baseline', output_path=args.output_path) <|file_sep|># coding=utf-8 import os import pandas as pd class Yarowsky: def __init__(self): self.pos_to_idx_dict = {'a': {i: idx + i * len(self.pos_to_idx_dict) + i * len(self.pos_to_idx_dict) * len( self.idx_to_pos_dict) for idx in range(len(pos))} for pos_i in range(len(self.idx_to_pos_dict)) for pos in self.idx_to_pos_dict[pos_i]} self.idx_to_pos_dict = {i: list(pos.values()) for i in range(len(pos))} def fit(self): df_a1a2a3a4a5a6a7a8a9b1b2b3b4b5b6b7b8b9c1c2c3c4c5c6c7c8c9d1d2d3d4d5d6d7d8d9e1e2e3e4e5e6e7e8e9f1f2f3f4f5f6f7f8f9g1g2g3g4g5g6g7g8g9 h1 h2 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