The Prudential Hong Kong Tennis Open: A Glimpse into Tomorrow's Matches
The Prudential Hong Kong Tennis Open, a prestigious event in the ATP Challenger Tour, is set to captivate tennis enthusiasts worldwide. With its unique setting and competitive field, this tournament offers a thrilling preview of emerging talent and seasoned professionals. As we approach tomorrow's matches, anticipation builds around the expert betting predictions that promise to add an extra layer of excitement. Let's delve into the details of what to expect.
Overview of the Tournament
Held annually in Hong Kong, the Prudential Hong Kong Tennis Open has become a cornerstone event for players looking to make their mark on the international stage. The tournament features both singles and doubles competitions, drawing participants from across the globe. Known for its fast-paced courts and vibrant atmosphere, it provides a perfect platform for athletes to showcase their skills.
Key Matches and Players to Watch
Tomorrow's lineup includes some of the most promising talents in men's tennis today. Among them are rising stars who have been making waves on the Challenger circuit. These players bring not only skill but also strategic acumen that makes each match unpredictable and thrilling.
- Player A: Known for his powerful serve and agility on the court, Player A is expected to be a formidable opponent in his upcoming match.
- Player B: With a reputation for exceptional baseline play, Player B is anticipated to deliver a captivating performance.
- Player C: A wildcard entry with an impressive track record in recent tournaments, Player C could surprise many with his tenacity and tactical prowess.
Betting Predictions: Insights from Experts
Expert analysts have provided their insights on tomorrow's matches, offering betting predictions that highlight potential outcomes based on current form and historical performance. These predictions are invaluable for enthusiasts looking to engage with the tournament beyond just watching.
- Match Prediction - Player A vs Player D: Analysts suggest that Player A has a slight edge due to his recent victories on similar surfaces.
- Match Prediction - Player B vs Player E: Given Player B's consistency at this level, he is favored to win in straight sets.
- Doubles Prediction - Team X vs Team Y: Team X is predicted to triumph owing to their excellent coordination and past success together.
Tactics and Strategies: What Sets Top Players Apart
Understanding the tactics employed by top players can enhance your appreciation of their performances. Here are some key strategies observed in previous tournaments:
- Serving Techniques: Effective serving can disrupt an opponent's rhythm. Many top players utilize varied spins and placements to gain an advantage.
- Rally Construction: Building points through strategic shot selection allows players to control rallies and dictate play tempo.
- Mental Fortitude: The ability to maintain focus under pressure often determines match outcomes, especially in tightly contested games.
The Role of Weather Conditions
Weather plays a significant role in outdoor tennis tournaments like the Prudential Hong Kong Tennis Open. Players must adapt their strategies based on conditions such as wind speed and humidity.
- Wind Impact: Wind can alter ball trajectory; players often adjust their shot placement accordingly.
- Humidity Effects: High humidity levels can affect grip and stamina; hydration becomes crucial during matches.
Fan Engagement: How You Can Participate
Fans have multiple ways to engage with tomorrow's matches beyond watching live or following live scores online:
<|repo_name|>fengyongzhi/taichi<|file_sep#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
@Author: hankcs
@Date: May-01-2018
@Filename: parser.py
@Last modified by: hankcs
@Last modified time: Jul-17-2019
"""
import logging
from collections import defaultdict
from .common import *
logger = logging.getLogger(__name__)
class Token(object):
def __init__(self):
self.token_type = None
self.surface = ''
self.lid = -1
self.start_pos = -1
self.end_pos = -1
self.head_id = -1
class ParseError(Exception):
def __init__(self):
super(ParseError,self).__init__()
class Parser(object):
# def __init__(self):
# pass
# @abstractmethod
# def parse(self,sentence):
# raise NotImplementedError()
# @abstractmethod
# def parse_sentences(self,sentences):
# raise NotImplementedError()
class DependencyParser(Parser):
def __init__(self,model_path=None,model=None,**kwargs):
if model_path is None:
assert model is not None
logger.info("Load dependency parser model from memory.")
self.model = model
return
logger.info("Load dependency parser model from file {}.".format(model_path))
# load model from file or pickle object.
if os.path.isfile(model_path) or isinstance(model_path,basestring):
try:
import joblib
logger.info("Using joblib.load()")
self.model = joblib.load(model_path)
except ImportError:
import pickle
logger.info("Using pickle.load()")
f = open(model_path,'rb')
try:
self.model=pickle.load(f)
finally:
f.close()
else:
# assume it is already loaded object
self.model=model
<|repo_name|>fengyongzhi/taichi<|file_sep#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
@Author: hankcs
@Date: Mar-20-2018
@Filename: seq2seq.py
@Last modified by: hankcs
@Last modified time: Jun-06-2019
"""
import logging
import torch.nn as nn
from .common import *
from .layers import *
from .optimizers import Optimizer
logger = logging.getLogger(__name__)
def pad_sequences(sequences,maxlen=None,padding='post',truncating='post',value=0.,dtype='float32'):
[1]: """Pads each sequence to the same length (length of the longest sequence).
[2]: If maxlen is provided, any sequence longer than maxlen is truncated to maxlen.
[3]: Based on keras.preprocessing.sequence.pad_sequences
[4]: Arguments:
[5]: sequences: list of lists where each element is a sequence
[6]: maxlen: int, maximum length
[7]: padding: str,
[8]: 'pre' or 'post': pad either before or after each sequence.
[9]: truncating: str,
[10]: 'pre' or 'post': remove values from sequences larger than maxlen either at the beginning or at the end of the sequences.
[11]: value: float,
[12]: padding value.
[13]: dtype : type,
[14]: if specified, will cast padded array dtype to so
[15]: Returns:
[16]: numpy array with dimensions (number_of_sequences,maxlen)
[17]: Raises:
[18]: ValueError:
[19]: In case of invalid values for `truncating` or `padding`,
[20]: or in case of invalid shape for a `sequences` entry.
"""
def build_vocab(sentences,vocab_size=10000,min_freq=1,start_token='',end_token='',pad_token='',oov_token='',
lowercase=False,dtype=torch.long):
def get_batch_generator(sentences,batch_size,max_len=None,pad=True,dtype=torch.long):
def create_seq2seq_model(vocab_size_enc,vocab_size_dec,input_length_enc,input_length_dec,output_length_dec,
hidden_dim=128,num_layers=2,bidirectional=False,dropout=0.,use_cuda=False,**kwargs):
def train_seq2seq_model(model,data_loader,val_data_loader,criterion,n_epochs=10,batch_size=64,
lr=0.001,warmup_steps=4000,warmup_method='linear',patience=None,min_delta=None,
grad_clip_norm=None,**kwargs):
if __name__ == '__main__':
<|file_septitle=Taichi NLP Toolkit Documentation
author=hankcs
theme=simple
== Installation
Taichi requires Python >= v3.
=== Install via pip
Taichi can be installed via pip:
bash
pip install taichi
=== Install via source code
Download source code:
bash
git clone https://github.com/hankcs/taichi.git
cd taichi
Install dependencies:
bash
pip install -r requirements.txt
Install taichi:
bash
python setup.py install --user
=== Install CUDA Toolkit (optional)
If you want GPU support then you need CUDA Toolkit >= v9.
=== Download pretrained models (optional)
You can download pretrained models using scripts under ``data`` folder.
For example,
* ``download_all.sh`` downloads all pretrained models.
* ``download_depparse.sh`` downloads all dependency parsers.
* ``download_ner.sh`` downloads all named entity recognizers.
Note that these scripts assume you have already downloaded data manually.
== Getting Started
Taichi provides easy-to-use APIs for natural language processing tasks.
Here we give examples about how we use Taichi for various tasks.
=== Text classification
The following example shows how we use Taichi for text classification task.
We use [http://www.cs.cornell.edu/people/pabo/movie-review-data/ movie review dataset] as training data.
[source]
----
>>> import taichi as tch
>>> train_data=tch.datasets.MovieReviewDataset('data/movie-reviews-train.csv')
>>> test_data=tch.datasets.MovieReviewDataset('data/movie-reviews-test.csv')
>>> vocab=tch.build_vocab(train_data)
>>> train_iter=tch.create_text_classification_batch_generator(train_data,batch_size=32)
>>> test_iter=tch.create_text_classification_batch_generator(test_data,batch_size=len(test_data),shuffle=False)
>>> model=tch.create_text_classification_model(vocab_size=len(vocab),embedding_dim=100,num_classes=2,
... hidden_dim=[256],dropout=[0.5])
>>> criterion=tch.nn.BCELoss()
>>> optimizer=tch.optim.Adam(lr=0.001)
>>> tch.train_text_classification_model(model,criterion,optmizer,n_epochs=10,batch_iter=train_iter,
... val_batch_iter=test_iter)
----
=== Machine translation
The following example shows how we use Taichi for machine translation task.
We use [https://www.manythings.org/anki/deu-eng.zip German English parallel corpus] as training data.
[source]
----
>>> train_data=tch.datasets.ParallelCorpusDataset('data/deu-eng/train.deu','data/deu-eng/train.eng')
>>> test_data=tch.datasets.ParallelCorpusDataset('data/deu-eng/test.deu','data/deu-eng/test.eng')
>>>
>>> vocab_src,tgt_vocab=tch.build_vocab(train_data,maxlen_src=50,maxlen_tgt=50,vocab_size=(10000,10000))
>>>
>>> train_iter=tch.create_machine_translation_batch_generator(train_data,batch_size=32,
... max_len_src=vocab_src.max_len,max_len_tgt=vocab_tgt.max_len)
>>>
>>> test_iter=tch.create_machine_translation_batch_generator(test_data,batch_size=len(test_data),
... max_len_src=vocab_src.max_len,max_len_tgt=vocab_tgt.max_len,
... shuffle=False)
>>>
>>> model=tch.create_seq2seq_model(vocab_src.size(),tgt_vocab.size(),vocab_src.max_len,tgt_vocab.max_len+1,
... hidden_dim=[256],num_layers=[2],dropout=[0.5])
>>>
>>> criterion=tch.nn.NLLLoss()
>>>
>>> optimizer=tch.optim.Adam(lr=.001)
>>>
>>> tch.train_seq2seq_model(model,criterion,optmizer,n_epochs=10,batch_iter=train_iter,
... val_batch_iter=test_iter)
----
=== Named entity recognition
The following example shows how we use Taichi for named entity recognition task.
We use [https://github.com/clab/dynet/blob/master/data/conll2003/en.tagger EN CoNLL2003 dataset] as training data.
[source]
----
>>>
>>>
>>>> train_data_file=open('data/conll2003/en.tagger.train').readlines()
>>>> dev_data_file=open('data/conll2003/en.tagger.dev').readlines()
>>>> test_data_file=open('data/conll2003/en.tagger.test').readlines()
>>>
>>>> vocab,tags=set(),set()
>>>> sentences_tags=[] # [(sentence,label)]
>>>> sentence=[]
>>>> tag=[]
>>>>
>>>> # process training data
>>>> mode='train'
>>>>
>>>> for line in train_data_file:
...... if len(line.strip())==0:
....... if mode=='train':
........ sentences_tags.append((sentence,list(tag)))
........ sentence=[]
........ tag=[]
....... continue
...... line=line.strip().split('t')
...... word,char=line[:]
...... label=label[-1]
...... vocab.add(word)
...... tags.add(label)
...... sentence.append(word)
...... tag.append(label)
....
.... # add last sentence which does not end with empty line
.... sentences_tags.append((sentence,list(tag)))
.... sentence=[]
.... tag=[]
....
....
....
>>>> mode='dev'
>>>
>>>> # process development data
>>>> dev_sentences_tags=[] # [(sentence,label)]
>>>> sentence=[]
>>>> tag=[]
>>>
>>>>>>> for line in dev_data_file:
......... if len(line.strip())==0:
............. if mode=='dev':
.................. dev_sentences_tags.append((sentence,list(tag)))
.................. sentence=[]
.................. tag=[]
............. continue
......... line=line.strip().split('t')
......... word,char=line[:]
......... label=label[-1]
......... sentence.append(word)
......... tag.append(label)
....
.... dev_sentences_tags.append((sentence,list(tag)))
.... sentence=[]
.... tag=[]
...
...
...
>>>>>>> mode='test'
>>>>>>> # process testing data
>>>>>>> test_sentences_tags=[] # [(sentence,label)]
>>>>>>> sentence=[]
>>>>>>> tag=[]
>>>>>>>> for line in test_data_file:
............. if len(line.strip())==0:
.................. if mode=='test':
..................... test_sentences_tags.append((sentence,list(tag)))
..................... sentence=[]
..................... tag=[]
................ continue
............. line=line.strip().split('t')
............. word,char=line[:]
............. label=label[-1]
............. sentence.append(word)
............. tag.append(label)
.......
....... test_sentences_tags.append((sentence,list(tag)))
....... sentence=[]
....... tag=[]
...
...
...
>>>>>>> vocab=dict(zip(vocab,(range(len(vocabs)))))
>>>>>>> tags=dict(zip(tags,(range(len(tags)))))
>>>>>>>> vocab['=']=len(vocabs)+1
>>>>>>>> tags['O']=len(tags)+1
>>>>>>>> print(len(vocabs),len(tags))
30*30+4*4+22*22+4*4+4*4+9*9+6*6+5*5+6*6+7*7+8*8 +102 +4 + len(tags)+1 len(tags)+1
>>>
>>>
Note that here we treat NER problem as token-level classification problem.
[source]
----
...
...
...
<<<<<<< vocab=dict(zip(list(vocabs),(range(len(vocabs)))))
<<<<<<< tags=dict(zip(list(tags),(range(len(tags)))))
<<<<<<< vocab['=']=len(vocabs)+1
<<<<<<< tags['O']=len(tags)+1
<<<<<<< print(len(vocabs),len(tags))
30*30+4*4+22*22+4*4+4*4+9*9+6*6+5*5+6*6+7*7+8 *8 +102 +4 + len(tags)+1 len(tags)+1
======= print(len(list(vocabs)),len(list(tags)))
31 *31 +5 *5 +23 *23 +5 *5 +5 *5 +10 *10 +7 *7 +6 *6 +7 *7 +8 *8 +
103 +5+len(list(tags))+1 len(list(tags))+1
======= tags['O']=len(list(tags))+1
======= print(len(list(vocabs)),len(list(tags)))
31 *31 +5 *5 +23 *23 +5 *5 +5 *5 +10 *10 +7 *7 +6 *6 +
107+len(list(tags))+1 len(list(tags))+1
======= vocab['=']=len(list(vocabs))+1
======= print(len(list(vocabs)),len(list(tags)))
31 *31 +
108+len(list(
tags))+11
====== print(len(dict.keys()),dict.values())
41 *
41
====== tags['O']=dict.values()+11
====== print(dict.keys(),dict.values())
41 *
41
=======
=======
=======
=======
==== Create vocabulary
==== Create iterators
[source]
----
<<<<<<< HEAD
<<<<<<< HEAD
<<<<<<< HEAD
<<<<<<< HEAD
<<<<<<<<<<<<< HEAD
||||||| merged common ancestors
||||||| merged common ancestors
||||||| merged common ancestors
||||||| merged common ancestors
||||||| merged common ancestors
=======
>>>>>>> parent of b82d62c... fix typo error (NER example) by hankcs · c906e93b
>>>>>>>>>>> Stashed changes
>>>>>>>>>>> Stashed changes
>>>>>>>>>>> Stashed changes
>>>>>>>>>>> Stashed changes
>>>>>>>>>>> Stashed changes
----
=======
>>>>>>> parent of b82d62c... fix typo error (NER example) by hankcs · c906e93b
>>>>>>>>>>> Stashed changes
>>>head_master_17f52a04_166a_43da_bf98_eebef51d684a
----
>>>master_a25fe666_02bb_49b0_bfd0_33c78fb27b55
----
----
----
----
----
>>train_iterator=create_ner_batch_generator(sentences_tags,vocab,tags,
... batch_size=batch_size,pad=True,padding_value=vocab['='])
>>dev_iterator=create_ner_batch_generator(dev_sentences_tags,vocab,tags,
... batch_size=len(dev_sentences_tags),pad=True,padding_value=vocab['='],shuffle=False)
>>test_iterator=create_ner_batch_generator(test_sentences_tags,vocab,tags,
... batch_size=len(test_sentences_tags),pad=True,padding_value=vocab['='],shuffle=False)
=== Create model ====
[source]
----
<<<<<<<<< HEAD
||||||| merged common ancestors
||||||| merged common ancestors
||||||| merged common ancestors
||||||| merged common ancestors
||||||| merged common ancestors
=======
>>>>>>> parent of b82d62c... fix typo error (NER example) by hankcs · c906e93b
>>>>>>>>>>> Stashed changes
>>>head_master_17f52a04_166a_43da_bf98_eebef51d684a
=======
=======
=======
=======
=======
=======
>>model=create_ner_model(input_dim=len(vocabulary),hidden_dims=[256],num_layers=num_layers,
... dropout_rates=[dropout],
... num_labels=len(labels))
=== Train model ====
[source]
----
<<<<<<<<< HEAD
||||||| merged common ancestors
||||||| merged common ancestors
||||||| merged common ancestors
||||||| merged common ancestors
||||||| merged common ancestors
=======
>>>>>>> parent of b82d62c... fix typo error (NER example) by hankcs · c906e93b
>>>>>>>>>>> Stashed changes
>>>head_master_17f52a04_166a_43da_bf98_eebef51d684a
-----
-----
-----
-----
-----
>>optimizer=torch.optim.Adam(params=model.parameters(),lr=.001)
>>loss_function=torch.nn.CrossEntropyLoss(ignore_index=vocabulary['='])
>>train_ner_model(n_epochs=n_epochs,model=model,criterion=criterion,
... optimizer_optimizer=train_iterator,val_iterator=val_iterator)
=== Evaluate model ====
We evaluate our trained model using F-score metric.
[source]
-----
<<<<<<< HEAD
||||||| merged common ancestors
||||||| merged common ancestors
||||||| merged common ancestors
||||||| merged common ancestors
||||||| merged common ancestors
=======
>>(labels_pred,scores)=predict_ner_labels(test_iterator,model)
>>(labels_gold,scores_gold)=zip(*[(label,score)for sent,label,score in zip(sentences_test,label_test,scores_test)])
>>(preds_gold_fscore)=compute_fscore(labels_pred,labels_gold)
>>(preds_scores_fscore)=compute_fscore(scores_pred,scores_gold)
>>(print(preds_gold_fscore))
F-score(P,R,F):{‘PER’: (85.71428571428571 ,88.,87.),‘LOC’: (92.,91.,91.),‘ORG’: (95.,94.,94.),‘MISC’: (84.,83.,83.)}
>>(print(preds_scores_fscore))
F-score(P,R,F):{‘PER’: (89.09090909090909 ,88.,88.),‘LOC’: (90.,91.,90.),‘ORG’: (96.,94.,95.),‘MISC’: (80.,79.,
81.)}
<|repo_name|>fengyongzhi/taichi<|file_sep>/requirements.txt
numpy~=1.19.2; python_version<'3'
numpy~=2020.12; python_version'>='3'
scikit_learn~=0.24; python_version<'3'
scikit_learn~=0.24; python_version'>='3'
nltk~=3; python_version<'3'
nltk~=3; python_version'>='3'<|repo_name|>fengyongzhi/taichi<|file_sepry call $PYTHONPATH=$PYTHONPATH:/Users/han/workspaces/nlp-toolkits/taichi/:$PYTHONPATH /Users/han/.pyenv/shims/python ./scripts/download_all.sh && exit $? || exit $?
#!/usr/bin/env bash
cd ../..
python setup.py develop --user --no-deps && exit $? || exit $?
cd examples/text-classification/
python main.py && exit $? || exit $?
cd ../../examples/machine-translation/
python main.py && exit $? || exit $?
cd ../../examples/named-entity-recognition/
python main.py && exit $? || exit $?
echo "All tests passed!" && echo "All tests passed!" | mailx -s "Taichitests passed" [email protected] && echo "All tests passed!" | mailx -s "Taichitests passed" [email protected] && echo "All tests passed!" | mailx -s "Taichitests passed" [email protected] && echo "All tests passed!"
exit $? || exit $
cd ..
ls *.sh | while read filename ; do sh "$filename"; done ; echo $? ; sleep .01 ; false ;
export PYTHONPATH=$PYTHONPATH:/Users/han/workspaces/nlp-toolkits/taichipy/:$PYTHONPATH ; cd .. ; ls *.sh | while read filename ; do sh "$filename"; done ; cd .. ;
export PYTHONPATH=$PYTHONPATH:/Users/han/workspaces/nlp-toolkits/taichipy/:$PYTHONPATH ; cd .. ; ls *.sh | while read filename ; do sh "$filename"; done ;
export PYTHONPATH=$PYTHONPATH:/Users/han/workspaces/nlp-toolkits/taichipy/:$PYTHONPATH ;
echo All tests passed! & mailx -s Taichitests passed [email protected] & mailx -s Taichitests passed [email protected] & mailx -s Taichitests passed [email protected] &
echo All tests passed!
exit $? || exit $
mailx -s Taichitests failed [email protected] &
mailx -s Taichitests failed [email protected] &
mailx -s Taichitests failed [email protected] &
exit $? || exit $
mailx -s Taichitests failed [email protected] & mailx -s Taichitests failed [email protected] & mailx -s Taichitests failed [email protected] &
exit $? || exit $
false ;
ls *.sh | while read filename ; do sh "$filename"; done ;
ls *.sh | while read filename ; do sh "$filename"; done ; sleep .01;
ls *.sh | while read filename ; do sh "$filename"; done ;
false ;
me="text-center">
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