Clickbait Challenge at SemEval 2023 - Clickbait Spoiling

我接下来会发题目和我写的代码给你，帮我修改代码以符合题目的全部要求，我自己会执行你给我的代码。
题目：
Task 1 on Spoiler Type Classification: The input is the clickbait post and the linked document. The task is to classify the spoiler type that the clickbait post warrants (either "phrase", "passage", "multi"). For each input, an output like {"uuid": "<UUID>", "spoilerType": "<SPOILER-TYPE>"} has to be generated where <SPOILER-TYPE> is either phrase, passage, or multi.
Task 2 on Spoiler Generation: The input is the clickbait post and the linked document (and, optional, the spoiler type if your approach uses this field). The task is to generate the spoiler for the clickbait post. For each input, an output like {"uuid": "<UUID>", "spoiler": "<SPOILER>"} has to be generated where <SPOILER> is the spoiler for the clickbait post.

Data [download]
The dataset contains the clickbait posts and manually cleaned versions of the linked documents, and extracted spoilers for each clickbait post (the dataset was constructed and published in the corresponding paper). Additionally, the spoilers are categorized into three types: short phrase spoilers, longer passage spoilers, and multiple non-consecutive pieces of text.

The training and validation data is available for download on zenodo. This dataset contains contains 3,200 posts for training (the file training.jsonl) and 800 posts for validation (the file validation.jsonl). After training and validation, systems are evaluated on 1,000 test posts.

Input Format
The data comes in JSON Lines format (.jsonl) where each line contains a clickbait post and the manually cleaned version of the linked document. For each line, the goal is to classify the spoiler type needed (task 1), and/or to generate the spoiler (task 2).

For each entry in the training and validation dataset, the following fields are available:

uuid: The uuid of the dataset entry.
postText: The text of the clickbait post which is to be spoiled.
targetParagraphs: The main content of the linked web page to classify the spoiler type (task 1) and to generate the spoiler (task 2). Consists of the paragraphs of manually extracted main content.
targetTitle: The title of the linked web page to classify the spoiler type (task 1) and to generate the spoiler (task 2).
targetUrl: The URL of the linked web page.
humanSpoiler: The human generated spoiler (abstractive) for the clickbait post from the linked web page. This field is only available in the training and validation dataset (not during test).
spoiler: The human extracted spoiler for the clickbait post from the linked web page. This field is only available in the training and validation dataset (not during test).
spoilerPositions: The position of the human extracted spoiler for the clickbait post from the linked web page. This field is only available in the training and validation dataset (not during test).
tags: The spoiler type (might be "phrase", "passage", or "multi") that is to be classified in task 1 (spoiler type classification). For task 1, this field is only available in the training and validation dataset (not during test). For task 2, this field is always available and can be used.
Some fields contain additional metainformation about the entry but are unused: postId, postPlatform, targetDescription, targetKeywords, targetMedia.
The following is a simplified entry in the dataset (line breaks added for readability):

{
  "uuid": "0af11f6b-c889-4520-9372-66ba25cb7657",
  "postText": ["Wes Welker Wanted Dinner With Tom Brady, But Patriots QB Had Better Idea"],
  "targetParagraphs": [
    "It’ll be just like old times this weekend for Tom Brady and Wes Welker.",
    "Welker revealed Friday morning on a Miami radio station that he contacted Brady because he’ll be in town for Sunday’s game between the New England Patriots and Miami Dolphins at Gillette Stadium. It seemed like a perfect opportunity for the two to catch up.",
    "But Brady’s definition of \"catching up\" involves far more than just a meal. In fact, it involves some literal \"catching\" as the Patriots quarterback looks to stay sharp during his four-game Deflategate suspension.",
    "\"I hit him up to do dinner Saturday night. He’s like, ‘I’m going to be flying in from Ann Arbor later (after the Michigan-Colorado football game), but how about that morning we go throw?’ \" Welker said on WQAM, per The Boston Globe. \"And I’m just sitting there, I’m like, ‘I was just thinking about dinner, but yeah, sure. I’ll get over there early and we can throw a little bit.’ \"",
    "Welker was one of Brady’s favorite targets for six seasons from 2007 to 2012. It’s understandable him and Brady want to meet with both being in the same area. But Brady typically is all business during football season. Welker probably should have known what he was getting into when reaching out to his buddy.",
    "\"That’s the only thing we really have planned,\" Welker said of his upcoming workout with Brady. \"It’s just funny. I’m sitting there trying to have dinner. ‘Hey, get your ass up here and let’s go throw.’ I’m like, ‘Aw jeez, man.’ He’s going to have me running like 2-minute drills in his backyard or something.\"",
    "Maybe Brady will put a good word in for Welker down in Foxboro if the former Patriots wide receiver impresses him enough."
  ],
  "targetTitle": "Wes Welker Wanted Dinner With Tom Brady, But Patriots QB Had A Better Idea",
  "targetUrl": "http://nesn.com/2016/09/wes-welker-wanted-dinner-with-tom-brady-but-patriots-qb-had-better-idea/",
  "spoiler": ["how about that morning we go throw?"],
  "spoilerPositions": [[[3, 151], [3, 186]]],
  "tags": ["passage"]
}
Output Format [validator]
The output format for task 1 and task 2 is identical but other fields are mandatory. Please submit your results in JSON Lines format producing one output line for each input instance.

Each line should have the following format: {"uuid": "<UUID>", "spoilerType": "<SPOILER-TYPE>", "spoiler": "<SPOILER>"}

where:

<UUID>is the uuid of the input instance.
<SPOILER-TYPE> is the spoiler type (might be "phrase", "passage", or "multi") to be predicted in task 1. This field is mandatory for task 1 but optional for task 2 (to indicate that your system used some type of spoiler type classification during the spoiler generation).
<SPOILER> is the generated spoiler to be produced in task 2. This field is mandatory for task 2.
We provide code and example outputs that you can validate your submissions in this github repository.
我现在所拥有的数据文件为：validation.jsonl、train.jsonl，两个文件的结构格式如下：
{"uuid": "0af11f6b-c889-4520-9372-66ba25cb7657", "postId": "532quh", "postText": ["Wes Welker Wanted Dinner With Tom Brady, But Patriots QB Had Better Idea"], "postPlatform": "reddit", "targetParagraphs": ["It’ll be just like old times this weekend for Tom Brady and Wes Welker.", "Welker revealed Friday morning on a Miami radio station that he contacted Brady because he’ll be in town for Sunday’s game between the New England Patriots and Miami Dolphins at Gillette Stadium. It seemed like a perfect opportunity for the two to catch up.", "But Brady’s definition of \"catching up\" involves far more than just a meal. In fact, it involves some literal \"catching\" as the Patriots quarterback looks to stay sharp during his four-game Deflategate suspension.", "\"I hit him up to do dinner Saturday night. He’s like, ‘I’m going to be flying in from Ann Arbor later (after the Michigan-Colorado football game), but how about that morning we go throw?’ \" Welker said on WQAM, per The Boston Globe. \"And I’m just sitting there, I’m like, ‘I was just thinking about dinner, but yeah, sure. I’ll get over there early and we can throw a little bit.’ \"", "Welker was one of Brady’s favorite targets for six seasons from 2007 to 2012. It’s understandable him and Brady want to meet with both being in the same area. But Brady typically is all business during football season. Welker probably should have known what he was getting into when reaching out to his buddy.", "\"That’s the only thing we really have planned,\" Welker said of his upcoming workout with Brady. \"It’s just funny. I’m sitting there trying to have dinner. ‘Hey, get your ass up here and let’s go throw.’ I’m like, ‘Aw jeez, man.’ He’s going to have me running like 2-minute drills in his backyard or something.\"", "Maybe Brady will put a good word in for Welker down in Foxboro if the former Patriots wide receiver impresses him enough."], "targetTitle": "Wes Welker Wanted Dinner With Tom Brady, But Patriots QB Had A Better Idea", "targetDescription": "It'll be just like old times this weekend for Tom Brady and Wes Welker. Welker revealed Friday morning on a Miami radio station that he contacted Brady because he'll be in town for Sunday's game between the New England Patriots and Miami Dolphins at Gillette Stadium.", "targetKeywords": "new england patriots, ricky doyle, top stories,", "targetMedia": ["http://pixel.wp.com/b.gif?v=noscript", "http://b.scorecardresearch.com/p?c1=2&c2=6783782&cv=2.0&cj=1", "http://s.wordpress.com/wp-includes/images/rss.png?m=1354137473h", "https://nesncom.files.wordpress.com/2017/03/paul-worrilow.jpg?w=400", "http://ssl-nesn-com-255369.c-col.com", "http://nesncom.files.wordpress.com/2015/07/yardbarker-fox-sidebar-logo.gif?w=300&h=33", "http://pixel.quantserve.com/pixel/p-0eFueme2jAuMI.gif", "https://s0.wp.com/wp-content/themes/vip/plugins/lazy-load/images/1x1.trans.gif", "https://nesncom.files.wordpress.com/2017/03/brandon-marshall.jpg?w=400", "https://nesncom.files.wordpress.com/2017/03/liverpool-vs-arsenal-2017-live-blog-premier-league-week-27.jpg?w=400", "https://nesncom.files.wordpress.com/2017/03/usatsi_9913527.jpg?w=400", "https://nesncom.files.wordpress.com/2017/03/david-price4.jpg?w=400", "https://nesncom.files.wordpress.com/2017/03/usatsi_9914527.jpg?w=400", "https://nesncom.files.wordpress.com/2017/03/eric-decker.jpg?w=400", "http://nesncom.files.wordpress.com/2015/06/transparent.gif?w=1&h=1", "https://nesncom.files.wordpress.com/2015/09/tom-brady-wes-welker.jpg", "https://nesncom.files.wordpress.com/2017/03/john-ross1.jpg?w=400", "https://d5nxst8fruw4z.cloudfront.net/atrk.gif?account=RPMTh1aUXR00Ug"], "targetUrl": "http://nesn.com/2016/09/wes-welker-wanted-dinner-with-tom-brady-but-patriots-qb-had-better-idea/", "provenance": {"source": "anonymized", "humanSpoiler": "They Threw A Football", "spoilerPublisher": "savedyouaclick"}, "spoiler": ["how about that morning we go throw?"], "spoilerPositions": [[[3, 151], [3, 186]]], "tags": ["passage"]}
大部分数据省略，让你知道这个数据结构是什么样的。
我的代码：
import json
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
from nltk.tokenize import word_tokenize
from nltk.lm import MLE
from nltk.lm.preprocessing import padded_everygram_pipeline


# Define a function to read in the jsonl file
def read_jsonl(input_path):
    data = []
    with open(input_path, 'r', encoding='utf-8') as f:
        for line in f:
            data.append(json.loads(line.rstrip('\n|\r')))
    return data


# Read the train and validation data
train_data = read_jsonl('train.jsonl')
validation_data = read_jsonl('validation.jsonl')

# Convert the data to pandas dataframes
train_df = pd.DataFrame(train_data)
validation_df = pd.DataFrame(validation_data)


# Task 1: Predicting spoiler tags
def train_model_task1(train_df):
    vectorizer = CountVectorizer()
    X_train = vectorizer.fit_transform(train_df['targetParagraphs'].apply(' '.join))
    y_train = train_df['tags']
    model = MultinomialNB()
    model.fit(X_train, y_train)
    return model, vectorizer

def predict_tags_task1(model, vectorizer, text):
    X_test = vectorizer.transform([text])
    predicted_tags = model.predict(X_test)
    return predicted_tags[0]


# Training the model for task 1
model_task1, vectorizer_task1 = train_model_task1(train_df)

# Print a prediction for task1
print(predict_tags_task1(model_task1, vectorizer_task1, ' '.join(validation_df['targetParagraphs'].iloc[0])))


# Task 2: Generating spoilers
def train_model_task2(texts):
    tokenized_text = [list(map(str.lower, word_tokenize(sent))) for sent in texts]
    train_data, padded_sents = padded_everygram_pipeline(3, tokenized_text)
    model = MLE(3) 
    model.fit(train_data, padded_sents)
    return model

def generate_spoiler_task2(model, text, n_words=10):
    tokenized_text = list(map(str.lower, word_tokenize(text)))
    starting_seq = tokenized_text[:2]
    spoiler = model.generate(n_words, text=starting_seq)
    return ' '.join(spoiler)


# Training the model for task 2
texts_task2 = train_df['targetParagraphs'].apply(' '.join).tolist()
model_task2 = train_model_task2(texts_task2)

# Print a generated spoiler for task2
print(generate_spoiler_task2(model_task2, texts_task2[0]))

我接下来会发题目，给我直接实现代码，不要任何省略。你也不用解释，除非我让你解释。我自己会执行你给我的代码。
题目：
Task 1 on Spoiler Type Classification: The input is the clickbait post and the linked document. The task is to classify the spoiler type that the clickbait post warrants (either "phrase", "passage", "multi"). For each input, an output like {"uuid": "<UUID>", "spoilerType": "<SPOILER-TYPE>"} has to be generated where <SPOILER-TYPE> is either phrase, passage, or multi.
Task 2 on Spoiler Generation: The input is the clickbait post and the linked document (and, optional, the spoiler type if your approach uses this field). The task is to generate the spoiler for the clickbait post. For each input, an output like {"uuid": "<UUID>", "spoiler": "<SPOILER>"} has to be generated where <SPOILER> is the spoiler for the clickbait post.

Data [download]
The dataset contains the clickbait posts and manually cleaned versions of the linked documents, and extracted spoilers for each clickbait post (the dataset was constructed and published in the corresponding paper). Additionally, the spoilers are categorized into three types: short phrase spoilers, longer passage spoilers, and multiple non-consecutive pieces of text.

The training and validation data is available for download on zenodo. This dataset contains contains 3,200 posts for training (the file training.jsonl) and 800 posts for validation (the file validation.jsonl). After training and validation, systems are evaluated on 1,000 test posts.

Input Format
The data comes in JSON Lines format (.jsonl) where each line contains a clickbait post and the manually cleaned version of the linked document. For each line, the goal is to classify the spoiler type needed (task 1), and/or to generate the spoiler (task 2).

For each entry in the training and validation dataset, the following fields are available:

uuid: The uuid of the dataset entry.
postText: The text of the clickbait post which is to be spoiled.
targetParagraphs: The main content of the linked web page to classify the spoiler type (task 1) and to generate the spoiler (task 2). Consists of the paragraphs of manually extracted main content.
targetTitle: The title of the linked web page to classify the spoiler type (task 1) and to generate the spoiler (task 2).
targetUrl: The URL of the linked web page.
humanSpoiler: The human generated spoiler (abstractive) for the clickbait post from the linked web page. This field is only available in the training and validation dataset (not during test).
spoiler: The human extracted spoiler for the clickbait post from the linked web page. This field is only available in the training and validation dataset (not during test).
spoilerPositions: The position of the human extracted spoiler for the clickbait post from the linked web page. This field is only available in the training and validation dataset (not during test).
tags: The spoiler type (might be "phrase", "passage", or "multi") that is to be classified in task 1 (spoiler type classification). For task 1, this field is only available in the training and validation dataset (not during test). For task 2, this field is always available and can be used.
Some fields contain additional metainformation about the entry but are unused: postId, postPlatform, targetDescription, targetKeywords, targetMedia.
The following is a simplified entry in the dataset (line breaks added for readability):

{
  "uuid": "0af11f6b-c889-4520-9372-66ba25cb7657",
  "postText": ["Wes Welker Wanted Dinner With Tom Brady, But Patriots QB Had Better Idea"],
  "targetParagraphs": [
    "It’ll be just like old times this weekend for Tom Brady and Wes Welker.",
    "Welker revealed Friday morning on a Miami radio station that he contacted Brady because he’ll be in town for Sunday’s game between the New England Patriots and Miami Dolphins at Gillette Stadium. It seemed like a perfect opportunity for the two to catch up.",
    "But Brady’s definition of \"catching up\" involves far more than just a meal. In fact, it involves some literal \"catching\" as the Patriots quarterback looks to stay sharp during his four-game Deflategate suspension.",
    "\"I hit him up to do dinner Saturday night. He’s like, ‘I’m going to be flying in from Ann Arbor later (after the Michigan-Colorado football game), but how about that morning we go throw?’ \" Welker said on WQAM, per The Boston Globe. \"And I’m just sitting there, I’m like, ‘I was just thinking about dinner, but yeah, sure. I’ll get over there early and we can throw a little bit.’ \"",
    "Welker was one of Brady’s favorite targets for six seasons from 2007 to 2012. It’s understandable him and Brady want to meet with both being in the same area. But Brady typically is all business during football season. Welker probably should have known what he was getting into when reaching out to his buddy.",
    "\"That’s the only thing we really have planned,\" Welker said of his upcoming workout with Brady. \"It’s just funny. I’m sitting there trying to have dinner. ‘Hey, get your ass up here and let’s go throw.’ I’m like, ‘Aw jeez, man.’ He’s going to have me running like 2-minute drills in his backyard or something.\"",
    "Maybe Brady will put a good word in for Welker down in Foxboro if the former Patriots wide receiver impresses him enough."
  ],
  "targetTitle": "Wes Welker Wanted Dinner With Tom Brady, But Patriots QB Had A Better Idea",
  "targetUrl": "http://nesn.com/2016/09/wes-welker-wanted-dinner-with-tom-brady-but-patriots-qb-had-better-idea/",
  "spoiler": ["how about that morning we go throw?"],
  "spoilerPositions": [[[3, 151], [3, 186]]],
  "tags": ["passage"]
}
Output Format [validator]
The output format for task 1 and task 2 is identical but other fields are mandatory. Please submit your results in JSON Lines format producing one output line for each input instance.

Each line should have the following format: {"uuid": "<UUID>", "spoilerType": "<SPOILER-TYPE>", "spoiler": "<SPOILER>"}

where:

<UUID>is the uuid of the input instance.
<SPOILER-TYPE> is the spoiler type (might be "phrase", "passage", or "multi") to be predicted in task 1. This field is mandatory for task 1 but optional for task 2 (to indicate that your system used some type of spoiler type classification during the spoiler generation).
<SPOILER> is the generated spoiler to be produced in task 2. This field is mandatory for task 2.
We provide code and example outputs that you can validate your submissions in this github repository.
我现在所拥有的数据文件为：validation.jsonl、train.jsonl，两个文件的结构格式如下：
{"uuid": "0af11f6b-c889-4520-9372-66ba25cb7657", "postId": "532quh", "postText": ["Wes Welker Wanted Dinner With Tom Brady, But Patriots QB Had Better Idea"], "postPlatform": "reddit", "targetParagraphs": ["It’ll be just like old times this weekend for Tom Brady and Wes Welker.", "Welker revealed Friday morning on a Miami radio station that he contacted Brady because he’ll be in town for Sunday’s game between the New England Patriots and Miami Dolphins at Gillette Stadium. It seemed like a perfect opportunity for the two to catch up.", "But Brady’s definition of \"catching up\" involves far more than just a meal. In fact, it involves some literal \"catching\" as the Patriots quarterback looks to stay sharp during his four-game Deflategate suspension.", "\"I hit him up to do dinner Saturday night. He’s like, ‘I’m going to be flying in from Ann Arbor later (after the Michigan-Colorado football game), but how about that morning we go throw?’ \" Welker said on WQAM, per The Boston Globe. \"And I’m just sitting there, I’m like, ‘I was just thinking about dinner, but yeah, sure. I’ll get over there early and we can throw a little bit.’ \"", "Welker was one of Brady’s favorite targets for six seasons from 2007 to 2012. It’s understandable him and Brady want to meet with both being in the same area. But Brady typically is all business during football season. Welker probably should have known what he was getting into when reaching out to his buddy.", "\"That’s the only thing we really have planned,\" Welker said of his upcoming workout with Brady. \"It’s just funny. I’m sitting there trying to have dinner. ‘Hey, get your ass up here and let’s go throw.’ I’m like, ‘Aw jeez, man.’ He’s going to have me running like 2-minute drills in his backyard or something.\"", "Maybe Brady will put a good word in for Welker down in Foxboro if the former Patriots wide receiver impresses him enough."], "targetTitle": "Wes Welker Wanted Dinner With Tom Brady, But Patriots QB Had A Better Idea", "targetDescription": "It'll be just like old times this weekend for Tom Brady and Wes Welker. Welker revealed Friday morning on a Miami radio station that he contacted Brady because he'll be in town for Sunday's game between the New England Patriots and Miami Dolphins at Gillette Stadium.", "targetKeywords": "new england patriots, ricky doyle, top stories,", "targetMedia": ["http://pixel.wp.com/b.gif?v=noscript", "http://b.scorecardresearch.com/p?c1=2&c2=6783782&cv=2.0&cj=1", "http://s.wordpress.com/wp-includes/images/rss.png?m=1354137473h", "https://nesncom.files.wordpress.com/2017/03/paul-worrilow.jpg?w=400", "http://ssl-nesn-com-255369.c-col.com", "http://nesncom.files.wordpress.com/2015/07/yardbarker-fox-sidebar-logo.gif?w=300&h=33", "http://pixel.quantserve.com/pixel/p-0eFueme2jAuMI.gif", "https://s0.wp.com/wp-content/themes/vip/plugins/lazy-load/images/1x1.trans.gif", "https://nesncom.files.wordpress.com/2017/03/brandon-marshall.jpg?w=400", "https://nesncom.files.wordpress.com/2017/03/liverpool-vs-arsenal-2017-live-blog-premier-league-week-27.jpg?w=400", "https://nesncom.files.wordpress.com/2017/03/usatsi_9913527.jpg?w=400", "https://nesncom.files.wordpress.com/2017/03/david-price4.jpg?w=400", "https://nesncom.files.wordpress.com/2017/03/usatsi_9914527.jpg?w=400", "https://nesncom.files.wordpress.com/2017/03/eric-decker.jpg?w=400", "http://nesncom.files.wordpress.com/2015/06/transparent.gif?w=1&h=1", "https://nesncom.files.wordpress.com/2015/09/tom-brady-wes-welker.jpg", "https://nesncom.files.wordpress.com/2017/03/john-ross1.jpg?w=400", "https://d5nxst8fruw4z.cloudfront.net/atrk.gif?account=RPMTh1aUXR00Ug"], "targetUrl": "http://nesn.com/2016/09/wes-welker-wanted-dinner-with-tom-brady-but-patriots-qb-had-better-idea/", "provenance": {"source": "anonymized", "humanSpoiler": "They Threw A Football", "spoilerPublisher": "savedyouaclick"}, "spoiler": ["how about that morning we go throw?"], "spoilerPositions": [[[3, 151], [3, 186]]], "tags": ["passage"]}
大部分数据省略，让你知道这个数据结构是什么样的。