#coding=utf-8

import os

import os.path

# 模型存放路徑

rootdir = r"H:\code\Model\bert-large-uncased-whole-word-masking-finetuned-squad"# 指明被遍歷的文件夾

for parent,dirnames,filenames in os.walk(rootdir):#三個參數：分別返回1.父目錄 2.所有文件夾名字（不含路徑） 3.所有文件名字

for filename in filenames:#文件名

# nameList=filename.split('.')

# print(nameList)

print(filename)

# filenew=nameList[0]+'.jpg'

# print(filenew)

#模型的名稱

newName=filename.replace('bert-large-uncased-whole-word-masking-finetuned-squad-','')

os.rename(os.path.join(parent,filename),os.path.join(parent,newName))#重命名

model_path="H:\\code\\Model\\bert-base-cased-finetuned-mrpc\\"

from transformers import pipeline

#使用當前模型+使用Tensorflow框架，默認應該是使用PYTORCH框架

nlp = pipeline("sentiment-analysis",model=model_path, tokenizer=model_path, framework="tf")

result = nlp("I hate you")[0]

print(f"label: {result['label']}, with score: {round(result['score'], 4)}")

result = nlp("I love you")[0]

print(f"label: {result['label']}, with score: {round(result['score'], 4)}")

model_path="H:\\code\\Model\\bert-base-cased-finetuned-mrpc\\"

#pytorch框架

from transformers import AutoTokenizer, AutoModelForSequenceClassification

import torch

tokenizer = AutoTokenizer.from_pretrained(model_path)

model = AutoModelForSequenceClassification.from_pretrained(model_path)

classes = ["not paraphrase", "is paraphrase"]

sequence_0 = "The company HuggingFace is based in New York City"

sequence_1 = "Apples are especially bad for your health"

sequence_2 = "HuggingFace's headquarters are situated in Manhattan"

paraphrase = tokenizer(sequence_0, sequence_2, return_tensors="pt")

not_paraphrase = tokenizer(sequence_0, sequence_1, return_tensors="pt")

paraphrase_classification_logits = model(**paraphrase).logits

not_paraphrase_classification_logits = model(**not_paraphrase).logits

paraphrase_results = torch.softmax(paraphrase_classification_logits, dim=1).tolist()[0]

not_paraphrase_results = torch.softmax(not_paraphrase_classification_logits, dim=1).tolist()[0]

# Should be paraphrase

for i in range(len(classes)):

print(f"{classes[i]}: {int(round(paraphrase_results[i] * 100))}%")

# Should not be paraphrase

for i in range(len(classes)):

print(f"{classes[i]}: {int(round(not_paraphrase_results[i] * 100))}%")

#tensorflow框架

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification

import tensorflow as tf

tokenizer = AutoTokenizer.from_pretrained(model_path)

model = TFAutoModelForSequenceClassification.from_pretrained(model_path)

classes = ["not paraphrase", "is paraphrase"]

sequence_0 = "The company HuggingFace is based in New York City"

sequence_1 = "Apples are especially bad for your health"

sequence_2 = "HuggingFace's headquarters are situated in Manhattan"

paraphrase = tokenizer(sequence_0, sequence_2, return_tensors="tf")

not_paraphrase = tokenizer(sequence_0, sequence_1, return_tensors="tf")

paraphrase_classification_logits = model(paraphrase)[0]

not_paraphrase_classification_logits = model(not_paraphrase)[0]

paraphrase_results = tf.nn.softmax(paraphrase_classification_logits, axis=1).numpy()[0]

not_paraphrase_results = tf.nn.softmax(not_paraphrase_classification_logits, axis=1).numpy()[0]

# Should be paraphrase

for i in range(len(classes)):

print(f"{classes[i]}: {int(round(paraphrase_results[i] * 100))}%")

# Should not be paraphrase

for i in range(len(classes)):

print(f"{classes[i]}: {int(round(not_paraphrase_results[i] * 100))}%")

model_path="H:\\code\\Model\\bert-large-uncased-whole-word-masking-finetuned-squad\\"

from transformers import pipeline

nlp = pipeline("question-answering",model=model_path, tokenizer=model_path)

context = r"""

Extractive Question Answering is the task of extracting an answer from a text given a question. An example of a

question answering dataset is the SQuAD dataset, which is entirely based on that task. If you would like to fine-tune

a model on a SQuAD task, you may leverage the examples/question-answering/run_squad.py script.

"""

result = nlp(question="What is extractive question answering?", context=context)

print(f"Answer: '{result['answer']}', score: {round(result['score'], 4)}, start: {result['start']}, end: {result['end']}")

result = nlp(question="What is a good example of a question answering dataset?", context=context)

print(f"Answer: '{result['answer']}', score: {round(result['score'], 4)}, start: {result['start']}, end: {result['end']}")

model_path="H:\\code\\Model\\bert-large-uncased-whole-word-masking-finetuned-squad\\"

#使用pytorch框架

from transformers import AutoTokenizer, AutoModelForQuestionAnswering

import torch

tokenizer = AutoTokenizer.from_pretrained(model_path)

model = AutoModelForQuestionAnswering.from_pretrained(model_path)

text = r"""

🤗 Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides general-purpose

architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet…) for Natural Language Understanding (NLU) and Natural

Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between

TensorFlow 2.0 and PyTorch.

"""

questions = [

"How many pretrained models are available in 🤗 Transformers?",

"What does 🤗 Transformers provide?",

"🤗 Transformers provides interoperability between which frameworks?",

]

for question in questions:

inputs = tokenizer(question, text, add_special_tokens=True, return_tensors="pt")

input_ids = inputs["input_ids"].tolist()[0]

text_tokens = tokenizer.convert_ids_to_tokens(input_ids)

outputs = model(**inputs)

answer_start_scores = outputs.start_logits

answer_end_scores = outputs.end_logits

answer_start = torch.argmax(

answer_start_scores

) # Get the most likely beginning of answer with the argmax of the score

answer_end = torch.argmax(answer_end_scores) + 1 # Get the most likely end of answer with the argmax of the score

answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end]))

print(f"Question: {question}")

print(f"Answer: {answer}")

#使用tensorflow框架

from transformers import AutoTokenizer, TFAutoModelForQuestionAnswering

import tensorflow as tf

tokenizer = AutoTokenizer.from_pretrained(model_path)

model = TFAutoModelForQuestionAnswering.from_pretrained(model_path)

text = r"""

🤗 Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides general-purpose

architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet…) for Natural Language Understanding (NLU) and Natural

Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between

TensorFlow 2.0 and PyTorch.

"""

questions = [

"How many pretrained models are available in 🤗 Transformers?",

"What does 🤗 Transformers provide?",

"🤗 Transformers provides interoperability between which frameworks?",

]

for question in questions:

inputs = tokenizer(question, text, add_special_tokens=True, return_tensors="tf")

input_ids = inputs["input_ids"].numpy()[0]

text_tokens = tokenizer.convert_ids_to_tokens(input_ids)

outputs = model(inputs)

answer_start_scores = outputs.start_logits

answer_end_scores = outputs.end_logits

answer_start = tf.argmax(

answer_start_scores, axis=1

).numpy()[0] # Get the most likely beginning of answer with the argmax of the score

answer_end = (

tf.argmax(answer_end_scores, axis=1) + 1

).numpy()[0] # Get the most likely end of answer with the argmax of the score

answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end]))

print(f"Question: {question}")

print(f"Answer: {answer}")

model_path="H:\\code\\Model\\distilbert-base-cased\\"

from transformers import pipeline

nlp = pipeline("fill-mask",model=model_path, tokenizer=model_path, framework="tf")

from pprint import pprint

pprint(nlp(f"HuggingFace is creating a {nlp.tokenizer.mask_token} that the community uses to solve NLP tasks."))

model_path="H:\\code\\Model\\distilbert-base-cased\\"

## 使用pytorch實現

from transformers import AutoModelWithLMHead, AutoTokenizer

import torch

tokenizer = AutoTokenizer.from_pretrained(model_path)

model = AutoModelWithLMHead.from_pretrained(model_path)

sequence = f"Distilled models are smaller than the models they mimic. Using them instead of the large versions would help {tokenizer.mask_token} our carbon footprint."

input = tokenizer.encode(sequence, return_tensors="pt")

mask_token_index = torch.where(input == tokenizer.mask_token_id)[1]

token_logits = model(input).logits

mask_token_logits = token_logits[0, mask_token_index, :]

top_5_tokens = torch.topk(mask_token_logits, 5, dim=1).indices[0].tolist()

for token in top_5_tokens:

print(sequence.replace(tokenizer.mask_token, tokenizer.decode([token])))

## 使用tensorflow實現

from transformers import TFAutoModelWithLMHead, AutoTokenizer

import tensorflow as tf

tokenizer = AutoTokenizer.from_pretrained(model_path)

model = TFAutoModelWithLMHead.from_pretrained(model_path)

sequence = f"Distilled models are smaller than the models they mimic. Using them instead of the large versions would help {tokenizer.mask_token} our carbon footprint."

input = tokenizer.encode(sequence, return_tensors="tf")

mask_token_index = tf.where(input == tokenizer.mask_token_id)[0, 1]

token_logits = model(input)[0]

mask_token_logits = token_logits[0, mask_token_index, :]

top_5_tokens = tf.math.top_k(mask_token_logits, 5).indices.numpy()

for token in top_5_tokens:

print(sequence.replace(tokenizer.mask_token, tokenizer.decode([token])))

model_path="H:\\code\\Model\\xlnet-base-cased\\"

from transformers import pipeline

text_generator = pipeline("text-generation",model=model_path, tokenizer=model_path, framework="tf")

print(text_generator("As far as I am concerned, I will", max_length=50, do_sample=False))

model_path="H:\\code\\Model\\xlnet-base-cased\\"

#使用pytorch版本

from transformers import AutoModelWithLMHead, AutoTokenizer

model = AutoModelWithLMHead.from_pretrained(model_path)

tokenizer = AutoTokenizer.from_pretrained(model_path)

# Padding text helps XLNet with short prompts - proposed by Aman Rusia in https://github.com/rusiaaman/XLNet-gen#methodology

PADDING_TEXT = """In 1991, the remains of Russian Tsar Nicholas II and his family

(except for Alexei and Maria) are discovered.

The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the

remainder of the story. 1883 Western Siberia,

a young Grigori Rasputin is asked by his father and a group of men to perform magic.

Rasputin has a vision and denounces one of the men as a horse thief. Although his

father initially slaps him for making such an accusation, Rasputin watches as the

man is chased outside and beaten. Twenty years later, Rasputin sees a vision of

the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,

with people, even a bishop, begging for his blessing. <eod> </s> <eos>"""

prompt = "Today the weather is really nice and I am planning on "

inputs = tokenizer.encode(PADDING_TEXT + prompt, add_special_tokens=False, return_tensors="pt")

prompt_length = len(tokenizer.decode(inputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=True))

outputs = model.generate(inputs, max_length=250, do_sample=True, top_p=0.95, top_k=60)

generated = prompt + tokenizer.decode(outputs[0])[prompt_length:]

print(generated)

#使用tensorflow版本

from transformers import TFAutoModelWithLMHead, AutoTokenizer

model = TFAutoModelWithLMHead.from_pretrained(model_path)

tokenizer = AutoTokenizer.from_pretrained(model_path)

# Padding text helps XLNet with short prompts - proposed by Aman Rusia in https://github.com/rusiaaman/XLNet-gen#methodology

PADDING_TEXT = """In 1991, the remains of Russian Tsar Nicholas II and his family

(except for Alexei and Maria) are discovered.

The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the

remainder of the story. 1883 Western Siberia,

a young Grigori Rasputin is asked by his father and a group of men to perform magic.

Rasputin has a vision and denounces one of the men as a horse thief. Although his

father initially slaps him for making such an accusation, Rasputin watches as the

man is chased outside and beaten. Twenty years later, Rasputin sees a vision of

the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,

with people, even a bishop, begging for his blessing. <eod> </s> <eos>"""

prompt = "Today the weather is really nice and I am planning on "

inputs = tokenizer.encode(PADDING_TEXT + prompt, add_special_tokens=False, return_tensors="tf")

prompt_length = len(tokenizer.decode(inputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=True))

outputs = model.generate(inputs, max_length=250, do_sample=True, top_p=0.95, top_k=60)

generated = prompt + tokenizer.decode(outputs[0])[prompt_length:]

print(generated)

model_path="H:\\code\\Model\\dbmdz\\bert-large-cased-finetuned-conll03-english\\"

tokenizer="H:\\code\\Model\\bert-base-cased\\"

from transformers import pipeline

nlp = pipeline("ner",model=model_path, tokenizer=tokenizer_path)

sequence = "Hugging Face Inc. is a company based in New York City. Its headquarters are in DUMBO, therefore very"

"close to the Manhattan Bridge which is visible from the window."

print(nlp(sequence))

model_path="H:\\code\\Model\\dbmdz\\bert-large-cased-finetuned-conll03-english\\"

tokenizer_path="H:\\code\\Model\\bert-base-cased\\"

##使用pytorch格式

from transformers import AutoModelForTokenClassification, AutoTokenizer

import torch

model = AutoModelForTokenClassification.from_pretrained(model_path)

tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)

label_list = [

"O", # Outside of a named entity

"B-MISC", # Beginning of a miscellaneous entity right after another miscellaneous entity

"I-MISC", # Miscellaneous entity

"B-PER", # Beginning of a person's name right after another person's name

"I-PER", # Person's name

"B-ORG", # Beginning of an organisation right after another organisation

"I-ORG", # Organisation

"B-LOC", # Beginning of a location right after another location

"I-LOC" # Location

]

sequence = "Hugging Face Inc. is a company based in New York City. Its headquarters are in DUMBO, therefore very" \

"close to the Manhattan Bridge."

# Bit of a hack to get the tokens with the special tokens

tokens = tokenizer.tokenize(tokenizer.decode(tokenizer.encode(sequence)))

inputs = tokenizer.encode(sequence, return_tensors="pt")

outputs = model(inputs).logits

predictions = torch.argmax(outputs, dim=2)

print([(token, label_list[prediction]) for token, prediction in zip(tokens, predictions[0].numpy())])

##使用tensorflow格式

from transformers import TFAutoModelForTokenClassification, AutoTokenizer

import tensorflow as tf

model = TFAutoModelForTokenClassification.from_pretrained(model_path)

tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)

label_list = [

"O", # Outside of a named entity

"B-MISC", # Beginning of a miscellaneous entity right after another miscellaneous entity

"I-MISC", # Miscellaneous entity

"B-PER", # Beginning of a person's name right after another person's name

"I-PER", # Person's name

"B-ORG", # Beginning of an organisation right after another organisation

"I-ORG", # Organisation

"B-LOC", # Beginning of a location right after another location

"I-LOC" # Location

]

sequence = "Hugging Face Inc. is a company based in New York City. Its headquarters are in DUMBO, therefore very" \

"close to the Manhattan Bridge."

# Bit of a hack to get the tokens with the special tokens

tokens = tokenizer.tokenize(tokenizer.decode(tokenizer.encode(sequence)))

inputs = tokenizer.encode(sequence, return_tensors="tf")

outputs = model(inputs)[0]

predictions = tf.argmax(outputs, axis=2)

print([(token, label_list[prediction]) for token, prediction in zip(tokens, predictions[0].numpy())])

model_path="H:\\code\\Model\\t5-base\\"

from transformers import pipeline

summarizer = pipeline("summarization",model=model_path, tokenizer=model_path)

ARTICLE = """ New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County, New York.

A year later, she got married again in Westchester County, but to a different man and without divorcing her first husband.

Only 18 days after that marriage, she got hitched yet again. Then, Barrientos declared "I do" five more times, sometimes only within two weeks of each other.

In 2010, she married once more, this time in the Bronx. In an application for a marriage license, she stated it was her "first and only" marriage.

Barrientos, now 39, is facing two criminal counts of "offering a false instrument for filing in the first degree," referring to her false statements on the

2010 marriage license application, according to court documents.

Prosecutors said the marriages were part of an immigration scam.

On Friday, she pleaded not guilty at State Supreme Court in the Bronx, according to her attorney, Christopher Wright, who declined to comment further.

After leaving court, Barrientos was arrested and charged with theft of service and criminal trespass for allegedly sneaking into the New York subway through an emergency exit, said Detective

Annette Markowski, a police spokeswoman. In total, Barrientos has been married 10 times, with nine of her marriages occurring between 1999 and 2002.

All occurred either in Westchester County, Long Island, New Jersey or the Bronx. She is believed to still be married to four men, and at one time, she was married to eight men at once, prosecutors say.

Prosecutors said the immigration scam involved some of her husbands, who filed for permanent residence status shortly after the marriages.

Any divorces happened only after such filings were approved. It was unclear whether any of the men will be prosecuted.

The case was referred to the Bronx District Attorney\'s Office by Immigration and Customs Enforcement and the Department of Homeland Security\'s

Investigation Division. Seven of the men are from so-called "red-flagged" countries, including Egypt, Turkey, Georgia, Pakistan and Mali.

Her eighth husband, Rashid Rajput, was deported in 2006 to his native Pakistan after an investigation by the Joint Terrorism Task Force.

If convicted, Barrientos faces up to four years in prison. Her next court appearance is scheduled for May 18.

"""

print(summarizer(ARTICLE, max_length=130, min_length=30, do_sample=False))

model_path="H:\\code\\Model\\t5-base\\"

#使用pytorch框架

from transformers import AutoModelWithLMHead, AutoTokenizer

model = AutoModelWithLMHead.from_pretrained(model_path)

tokenizer = AutoTokenizer.from_pretrained(model_path)

# T5 uses a max_length of 512 so we cut the article to 512 tokens.

inputs = tokenizer.encode("summarize: " + ARTICLE, return_tensors="pt", max_length=512)

outputs = model.generate(inputs, max_length=150, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True)

# print(outputs)

print(tokenizer.decode(outputs[0]))

#使用tensorflow框架

from transformers import TFAutoModelWithLMHead, AutoTokenizer

model = TFAutoModelWithLMHead.from_pretrained(model_path)

tokenizer = AutoTokenizer.from_pretrained(model_path)

# T5 uses a max_length of 512 so we cut the article to 512 tokens.

inputs = tokenizer.encode("summarize: " + ARTICLE, return_tensors="tf", max_length=512)

outputs = model.generate(inputs, max_length=150, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True)

model_path="H:\\code\\Model\\t5-base\\"

from transformers import pipeline

translator = pipeline("translation_en_to_de",model=model_path, tokenizer=model_path)

print(translator("Hugging Face is a technology company based in New York and Paris", max_length=40))

model_path="H:\\code\\Model\\distilbert-base-uncased\\"

#pytorch方式

from transformers import DistilBertConfig, DistilBertTokenizer, DistilBertForSequenceClassification

config = DistilBertConfig(n_heads=8, dim=512, hidden_dim=4*512)

tokenizer = DistilBertTokenizer.from_pretrained(model_path)

model = DistilBertForSequenceClassification(config)

#tensorflow方式

from transformers import DistilBertConfig, DistilBertTokenizer, TFDistilBertForSequenceClassification

config = DistilBertConfig(n_heads=8, dim=512, hidden_dim=4*512)

tokenizer = DistilBertTokenizer.from_pretrained(model_path)

model = TFDistilBertForSequenceClassification(config)

model_name="H:\\code\\Model\\distilbert-base-uncased\\"

#pytorch方式

from transformers import DistilBertConfig, DistilBertTokenizer, DistilBertForSequenceClassification

#model_name = "distilbert-base-uncased"

model = DistilBertForSequenceClassification.from_pretrained(model_name, num_labels=10)

tokenizer = DistilBertTokenizer.from_pretrained(model_name)

#tensorflow方式

from transformers import DistilBertConfig, DistilBertTokenizer, TFDistilBertForSequenceClassification

#model_name = "distilbert-base-uncased"

model = TFDistilBertForSequenceClassification.from_pretrained(model_name, num_labels=10)

tokenizer = DistilBertTokenizer.from_pretrained(model_name)

#模型保存

##對模型進行微調后，可以通過以下方式將其與標記器一起保存：

save_directory="./save/"

tokenizer.save_pretrained(save_directory)

model.save_pretrained(save_directory)

#模型加載

##TensorFlow模型中加載已保存的PyTorch模型

tokenizer = AutoTokenizer.from_pretrained(save_directory)

model = TFAutoModel.from_pretrained(save_directory, from_pt=True)

##PyTorch模型中加載已保存的TensorFlow模型

tokenizer = AutoTokenizer.from_pretrained(save_directory)

model = AutoModel.from_pretrained(save_directory, from_tf=True)