The Rise of the Football U18 Professional Development League Cup
The Football U18 Professional Development League Cup is an exciting stage for young talents across England. Group H, in particular, showcases some of the most promising players in the country, each vying for a spot in the next round and a chance to make their mark on the professional scene. With matches updated daily, fans and bettors alike have a constant stream of action to follow, making this league an essential watch for anyone interested in the future of football.
As these young athletes step onto the pitch, they carry not only their dreams but also the hopes of their clubs and supporters. The matches are not just about winning; they are about development, learning, and growth. This is where future stars are born, where potential is nurtured, and where the foundations of professional careers are laid.
Understanding Group H Dynamics
Group H is composed of some of England's most competitive and ambitious clubs. Each team brings a unique style and strategy to the table, making every match unpredictable and thrilling. The diversity in playing styles—from aggressive attacking football to solid defensive tactics—ensures that there is never a dull moment.
- Team A: Known for their dynamic forwards and quick transitions, Team A has been a formidable force in previous seasons.
- Team B: With a focus on youth development, Team B has produced several top-tier players who have gone on to shine at higher levels.
- Team C: Renowned for their tactical discipline, Team C often surprises opponents with their strategic depth.
- Team D: A relatively new entrant, Team D has quickly gained attention for their innovative playing style and fearless approach.
Daily Match Updates and Expert Analysis
Keeping up with the daily matches in Group H is essential for fans and bettors. Each game provides fresh insights into team strategies, player performances, and emerging talents. Our platform offers comprehensive match updates, including live scores, detailed analyses, and expert predictions to keep you informed every step of the way.
Our expert analysts break down each match, highlighting key players to watch, potential game-changers, and tactical battles that could decide the outcome. Whether you're a seasoned bettor or a casual fan, our insights provide valuable information to enhance your viewing experience.
Expert Betting Predictions
Betting on the Football U18 Professional Development League Cup can be both exciting and rewarding. Our expert betting predictions are based on thorough analysis of team form, player statistics, head-to-head records, and other critical factors. We provide daily predictions to help you make informed decisions and maximize your chances of success.
- Prediction Models: Utilizing advanced algorithms and data analytics, our models offer precise predictions tailored to each match.
- Odds Analysis: We provide a detailed breakdown of odds from various bookmakers, helping you identify value bets.
- Expert Tips: Our seasoned analysts share tips and strategies based on their extensive experience in sports betting.
Spotlight on Emerging Talents
One of the most exciting aspects of following Group H is witnessing the rise of emerging talents. These young players bring energy, skill, and passion to the pitch, often delivering performances that captivate audiences. Our spotlight section highlights these rising stars, offering profiles that delve into their backgrounds, playing styles, and career aspirations.
- Talent Profiles: Detailed profiles of standout players from each team.
- Player Progression: Tracking the development of key players throughout the season.
- Arcs to Watch: Identifying players with potential to break into professional leagues.
Tactical Insights and Match Previews
Understanding the tactical nuances of each match can significantly enhance your appreciation of the game. Our match previews provide an in-depth look at the strategies teams are likely to employ, potential line-ups, and key battles that could influence the outcome. Whether you're interested in formations, set-piece strategies, or individual matchups, our previews cover all aspects.
- Formation Analysis: Examining how different formations can impact team performance.
- Key Matchups: Highlighting critical player duels that could swing the game.
- Injury Updates: Keeping you informed about player availability and potential impact on team dynamics.
The Role of Coaches in Player Development
Coaches play a pivotal role in shaping young players' careers. In Group H, experienced coaches work tirelessly to develop their teams' skills and instill a winning mentality. Their expertise not only influences match outcomes but also prepares players for future challenges at higher levels.
- Career Pathways: How coaches guide players through their development journey.
- Mentorship: The importance of mentorship in nurturing young talent.
- Tactical Education: Teaching players about different tactical approaches and adaptability on the field.
Fan Engagement and Community Building
cweiliu1/RRS-Transformer<|file_sep|>/transformer/transformer.py
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import math
import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
def get_clones(module,n):
return nn.ModuleList([copy.deepcopy(module) for _ in range(n)])
class MultiHeadAttention(nn.Module):
def __init__(self,d_model,n_heads,d_k,d_v):
super().__init__()
self.d_model,self.n_heads,self.d_k,self.d_v = d_model,n_heads,d_k,d_v
self.q_linear = nn.Linear(d_model,d_k*n_heads)
self.v_linear = nn.Linear(d_model,d_v*n_heads)
self.k_linear = nn.Linear(d_model,d_k*n_heads)
self.dropout = nn.Dropout(p=0.1)
self.out = nn.Linear(n_heads*d_v,d_model)
def forward(self,q,k,v,out_mask=None):
batch_size = q.size(0)
q = self.q_linear(q).view(batch_size,self.n_heads,self.d_k)
k = self.k_linear(k).view(batch_size,self.n_heads,self.d_k)
v = self.v_linear(v).view(batch_size,self.n_heads,self.d_v)
scores = torch.bmm(q,k.transpose(1,-1))/math.sqrt(self.d_k)
if out_mask is not None:
scores.masked_fill_(out_mask,-1e9)
scores=self.dropout(F.softmax(scores,dim=-1))
output=torch.bmm(scores,v).transpose(1,0).contiguous().view(batch_size,-1,self.n_heads*self.d_v)
return self.out(output)
class FeedForward(nn.Module):
def __init__(self,d_model,d_ff):
super().__init__()
self.w_1 = nn.Linear(d_model,d_ff)
self.w_2 = nn.Linear(d_ff,d_model)
def forward(self,x):
return self.w_2(F.relu(self.w_1(x)))
class PositionalEncoding(nn.Module):
def __init__(self,d_model,max_len=5000):
super().__init__()
p_e = torch.zeros(max_len,d_model)
position=np.arange(0,max_len).reshape(-1,1)
div_term=torch.exp(torch.arange(0,d_model,reveresed=True,dim=0)*(-math.log(10000.0)/d_model))
p_e[:,0::2]=torch.sin(position*div_term[0::2])
p_e[:,1::2]=torch.cos(position*div_term[1::2])
def forward(self,x):
return x+self.pe[:x.size(0)]
class SublayerConnection(nn.Module):
def __init__(self,size,dropout):
super().__init__()
def forward(self,x):
class EncoderLayer(nn.Module):
def __init__(self,size,n_head,d_ff,norm_dropout,residual_dropout):
def get_attn_pad_mask(seq_q,pad_token,pad_mask=True):
def get_attn_subsequent_mask(seq):
def get_non_pad_mask(seq,pad_token=0):
def get_sinusoid_encoding_table(n_position,n_dim,scale=True):
def get_position_angle_vec(position,i,d_model):
def cal_angle(position,i,d_model):
def get_posiiton_embedding(pos_seq,n_position,n_dim,scale=True):
def Embedding(vocab_size,n_dim,pad_token=None):
class Encoder(nn.Module):
def __init__(self,vocab_size,n_layers,n_head,d_k,d_v,d_ff,input_dropout,residual_dropout,norm_dropout):
<|file_sep|># RRS-Transformer
Pytorch Implementation for "RRS-Transformer: An Efficient Transformer-Based Model for Dense Retrieval" (SIGIR'20)
[https://dl.acm.org/doi/10.1145/3397271.3401094](https://dl.acm.org/doi/10.1145/3397271.3401094)
**Contact**: [Chengwei Liu](https://github.com/cweiliu1) ([[email protected]]([email protected]))
**Acknowledgement**: This code borrows some components from [Huggingface Transformers](https://github.com/huggingface/transformers), [DeepPavlov](https://github.com/deepmipt/DeepPavlov), [PyTorch-NLP](https://github.com/jadore801120/attention-is-all-you-need-pytorch)
## Install
shell script
git clone https://github.com/cweiliu1/RRS-Transformer.git
cd RRS-Transformer/
pip install -r requirements.txt
## Usage
### Train
shell script
python3 train.py
--data_dir /path/to/train_data
--save_dir /path/to/save_directory
--bert_config_file /path/to/bert_config.json
--vocab_file /path/to/vocab.txt
--do_train
--max_seq_length=512
--do_lower_case
--batch_size=64
--learning_rate=3e-5
--num_train_epochs=3
--warmup_steps=10000
--weight_decay=0.01
### Evaluate
shell script
python3 eval.py --bert_config_file /path/to/bert_config.json --vocab_file /path/to/vocab.txt --do_lower_case --output_dir /path/to/output_directory --load_checkpoint /path/to/checkpoint.pt --data_dir /path/to/test_data --batch_size=64 --max_seq_length=512
### Inference
shell script
python3 inference.py --bert_config_file /path/to/bert_config.json --vocab_file /path/to/vocab.txt --do_lower_case --output_dir /path/to/output_directory --load_checkpoint /path/to/checkpoint.pt --query_path /path/to/query.txt --doc_path /path/to/doc.txt --batch_size=64 --max_seq_length=512
## Model Zoo
|Model Name|Size(MB)|# Params|BERT-base-finetune|BERT-base-pairwise|RRS-Transformer|
|-|-|-|-|-|-|
|[RRS-Transformer (Base)](https://drive.google.com/file/d/13LHb8lJZgPbHJWvQo8N8YjT_zKozG7eA/view?usp=sharing)|21.6|110M|99%|92%|-|
|[RRS-Transformer (Large)](https://drive.google.com/file/d/16qfOyBjfdiMqVZ3w5q4B_I8qVbPIFyAC/view?usp=sharing)|42|335M|-|-|-|
<|file_sep|># -*- coding: utf-8 -*-
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import json
from tqdm import tqdm
from util import file_util
class QueryDocIterator(object):
def __init__(self,
query_path,
doc_path,
max_doc_num=None,
max_query_length=None,
max_doc_length=None,
use_random=False,
use_cache=False,
cache_path=None,
data_format='jsonl'):
super(QueryDocIterator).__init__()
if use_cache:
try:
cache_data = json.load(open(cache_path))
except Exception:
cache_data = None
if cache_data:
print('Using cached data...')
self._query_doc_iterable = cache_data['query_doc_iterable']
else:
print('No cached data found...')
query_iterable = file_util.read_list(query_path)
if max_query_length is not None:
query_iterable = filter(lambda x: len(x.split()) <= max_query_length,
query_iterable)
doc_iterable_list = []
if data_format == 'jsonl':
doc_iterable_list.append(file_util.read_jsonl(doc_path))
elif data_format == 'tsv':
doc_iterable_list.append(file_util.read_tsv(doc_path))
if len(doc_iterable_list) != len(set(doc_iterable_list)):
raise ValueError('There exist same document(s) in documents!')
# Randomly sample documents if necessary.
if use_random:
import random
random.shuffle(doc_iterable_list[0])
# Truncate documents if necessary.
if max_doc_num is not None:
doc_iterable_list[0] = doc_iterable_list[0][:max_doc_num]
if max_doc_length is not None:
doc_iterable_list[0] = filter(lambda x: len(x['text'].split()) <= max_doc_length,
doc_iterable_list[0])
# Make sure queries have corresponding documents.
assert len(query_iterable) == len(doc_iterable_list[0]), (
'Number of queries should be equal '
'to number of documents!')
self._query_doc_iterable = []
for q_idx in tqdm(range(len(query_iterable))):
self._query_doc_iterable.append(
{'query': query_iterable[q_idx], 'doc': []})
for d_idx in range(len(doc_iterable_list)):
self._query_doc_iterable[q_idx]['doc'].append(
{'text': doc_iterable_list[d_idx][q_idx]['text'],
'label': int(
doc_iterable_list[d_idx][q_idx]['label'])})
# Cache data if necessary.
if use_cache:
print('Caching data...')
cache_data = {'query_doc_iterable': self._query_doc_iterable}
json.dump(cache_data,
open(cache_path + '.tmp', 'w'),
ensure_ascii=False,
indent=4)
file_util.replace(cache_path + '.tmp', cache_path)
# Build index.
self._index_builder()
self._index_dict = {}
self._build_index()
# Reset index pointer.
self._cur_index_pointer = -1
print('Number of queries: {}'.format(len(self._query_doc_iterable)))
def _index_builder(self):
# Build index list.
self._index_list = []
for idx in range(len(self._query_doc_iterable)):
self._index_list.append(idx)
# Shuffle index list if necessary.
import random
random.shuffle(self._index_list)
def _build_index(self):
# Initialize index dictionary.
for idx in range(len(self._query_doc_iterable)):
self._index_dict[idx] = idx
# Build index dictionary.
for idx in range(len(self._index_list)):
cur_index_pointer = idx % len(self._index_dict.keys())
while cur_index_pointer not in self._index_dict.keys():
cur_index_pointer += len(self._index_dict.keys())
self._index_dict[self._index_list[idx]] = cur_index_pointer
def reset_index_pointer(self):
"""Reset index pointer."""
self._cur_index_pointer = -1
def next_batch(self,
batch_size,
mode='pairwise',
add_neg=True,
neg_num=50,
neg_pool=None):
"""Get next batch.
Args:
batch_size: int value indicates batch size.
mode: string value indicates how to generate training samples.
add_neg: bool value indicates whether to add negative samples.
neg_num: int value indicates number of negative samples per positive sample.
neg_pool: list value indicates negative pool.
Returns:
A tuple (batch_queries,batch_docs,batch_labels).
batch_queries: list value indicates query texts.
batch_docs: list value indicates document texts.
batch_labels: list value indicates labels (positive or negative).
"""
assert mode in ['pairwise', 'pointwise'], (
'Invalid mode! Please select either pairwise or pointwise.')
batch_queries = []
batch_docs = []
batch_labels = []
while True:
# Update index pointer if necessary.
self._cur_index_pointer += batch_size
if self._cur_index_pointer >= len(self._index_dict.keys()):
print('One epoch done!')
print('Shuffling data...')
# Shuffle index list if necessary.
import random
random.shuffle(self._index_list)
# Rebuild index dictionary.
print('Rebuilding index...')
self._build_index()
# Reset index pointer.
print('Resetting index pointer...')
self.reset_index_pointer()
# Break loop since no more samples left now!
break
cur_batch_indices_set = set()
for _ in range(batch_size):
# Get current index from index dictionary.
cur_batch_indices_set.add(
list(self._index_dict.keys())[
list(self._index_dict.values()).index
