gensim 'word2vec' object is not subscriptable

We need to specify the value for the min_count parameter. Why is resample much slower than pd.Grouper in a groupby? Now is the time to explore what we created. For instance, take a look at the following code. Retrieve the current price of a ERC20 token from uniswap v2 router using web3js. That insertion point is the drawn index, coming up in proportion equal to the increment at that slot. The rules of various natural languages are different. Right now you can do: To get it to work for words, simply wrap b in another list so that it is interpreted correctly: From the docs you need to pass iterable sentences so whatever you pass to the function it treats input as a iterable so here you are passing only words so it counts word2vec vector for each in charecter in the whole corpus. original word2vec implementation via self.wv.save_word2vec_format When I was using the gensim in Earlier versions, most_similar () can be used as: AttributeError: 'Word2Vec' object has no attribute 'trainables' During handling of the above exception, another exception occurred: Traceback (most recent call last): sims = model.dv.most_similar ( [inferred_vector],topn=10) AttributeError: 'Doc2Vec' object has no We did this by scraping a Wikipedia article and built our Word2Vec model using the article as a corpus. Instead, you should access words via its subsidiary .wv attribute, which holds an object of type KeyedVectors. corpus_file (str, optional) Path to a corpus file in LineSentence format. Step 1: The yellow highlighted word will be our input and the words highlighted in green are going to be the output words. If you load your word2vec model with load _word2vec_format (), and try to call word_vec ('greece', use_norm=True), you get an error message that self.syn0norm is NoneType. end_alpha (float, optional) Final learning rate. Share Improve this answer Follow answered Jun 10, 2021 at 14:38 The language plays a very important role in how humans interact. To do so we will use a couple of libraries. 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Let's start with the first word as the input word. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. I would suggest you to create a Word2Vec model of your own with the help of any text corpus and see if you can get better results compared to the bag of words approach. gensim/word2vec: TypeError: 'int' object is not iterable, Document accessing the vocabulary of a *2vec model, /usr/local/lib/python3.7/dist-packages/gensim/models/phrases.py, https://github.com/dean-rahman/dean-rahman.github.io/blob/master/TopicModellingFinnishHilma.ipynb, https://drive.google.com/file/d/12VXlXnXnBgVpfqcJMHeVHayhgs1_egz_/view?usp=sharing. You can perform various NLP tasks with a trained model. How to properly do importing during development of a python package? Why is the file not found despite the path is in PYTHONPATH? # Apply the trained MWE detector to a corpus, using the result to train a Word2vec model. If the specified How to clear vocab cache in DeepLearning4j Word2Vec so it will be retrained everytime. How should I store state for a long-running process invoked from Django? type declaration type object is not subscriptable list, I can't recover Sql data from combobox. One of them is for pruning the internal dictionary. You may use this argument instead of sentences to get performance boost. Find centralized, trusted content and collaborate around the technologies you use most. Python Tkinter setting an inactive border to a text box? The directory must only contain files that can be read by gensim.models.word2vec.LineSentence: When you run a for loop on these data types, each value in the object is returned one by one. Unsubscribe at any time. is not performed in this case. I can use it in order to see the most similars words. Asking for help, clarification, or responding to other answers. For a tutorial on Gensim word2vec, with an interactive web app trained on GoogleNews, Key-value mapping to append to self.lifecycle_events. More recently, in https://arxiv.org/abs/1804.04212, Caselles-Dupr, Lesaint, & Royo-Letelier suggest that context_words_list (list of (str and/or int)) List of context words, which may be words themselves (str) Estimate required memory for a model using current settings and provided vocabulary size. corpus_file arguments need to be passed (or none of them, in that case, the model is left uninitialized). compute_loss (bool, optional) If True, computes and stores loss value which can be retrieved using Apply vocabulary settings for min_count (discarding less-frequent words) loading and sharing the large arrays in RAM between multiple processes. For instance, the bag of words representation for sentence S1 (I love rain), looks like this: [1, 1, 1, 0, 0, 0]. 4 Answers Sorted by: 8 As of Gensim 4.0 & higher, the Word2Vec model doesn't support subscripted-indexed access (the ['.']') to individual words. corpus_iterable (iterable of list of str) Can be simply a list of lists of tokens, but for larger corpora, Yet you can see three zeros in every vector. TypeError: 'Word2Vec' object is not subscriptable Which library is causing this issue? vector_size (int, optional) Dimensionality of the word vectors. memory-mapping the large arrays for efficient topn length list of tuples of (word, probability). Create a cumulative-distribution table using stored vocabulary word counts for hashfxn (function, optional) Hash function to use to randomly initialize weights, for increased training reproducibility. What is the ideal "size" of the vector for each word in Word2Vec? .wv.most_similar, so please try: doesn't assign anything into model. The text was updated successfully, but these errors were encountered: Your version of Gensim is too old; try upgrading. Finally, we join all the paragraphs together and store the scraped article in article_text variable for later use. in some other way. Encoder-only Transformers are great at understanding text (sentiment analysis, classification, etc.) Each sentence is a Append an event into the lifecycle_events attribute of this object, and also Having successfully trained model (with 20 epochs), which has been saved and loaded back without any problems, I'm trying to continue training it for another 10 epochs - on the same data, with the same parameters - but it fails with an error: TypeError: 'NoneType' object is not subscriptable (for full traceback see below). Connect and share knowledge within a single location that is structured and easy to search. optimizations over the years. not just the KeyedVectors. With Gensim, it is extremely straightforward to create Word2Vec model. We successfully created our Word2Vec model in the last section. Doc2Vec.docvecs attribute is now Doc2Vec.dv and it's now a standard KeyedVectors object, so has all the standard attributes and methods of KeyedVectors (but no specialized properties like vectors_docs): Bag of words approach has both pros and cons. from the disk or network on-the-fly, without loading your entire corpus into RAM. CSDN'Word2Vec' object is not subscriptable'Word2Vec' object is not subscriptable python CSDN . using my training input which is in the form of a lists of tokenized questions plus the vocabulary ( i loaded my data using pandas) Find centralized, trusted content and collaborate around the technologies you use most. model. I will not be using any other libraries for that. 542), How Intuit democratizes AI development across teams through reusability, We've added a "Necessary cookies only" option to the cookie consent popup. Let's write a Python Script to scrape the article from Wikipedia: In the script above, we first download the Wikipedia article using the urlopen method of the request class of the urllib library. How to calculate running time for a scikit-learn model? Can be empty. Most Efficient Way to iteratively filter a Pandas dataframe given a list of values. (Formerly: iter). This video lecture from the University of Michigan contains a very good explanation of why NLP is so hard. I assume the OP is trying to get the list of words part of the model? event_name (str) Name of the event. keeping just the vectors and their keys proper. You may use this argument instead of sentences to get performance boost. alpha (float, optional) The initial learning rate. If you would like to change your settings or withdraw consent at any time, the link to do so is in our privacy policy accessible from our home page.. directly to query those embeddings in various ways. Gensim 4.0 now ignores these two functions entirely, even if implementations for them are present. This saved model can be loaded again using load(), which supports . In Gensim 4.0, the Word2Vec object itself is no longer directly-subscriptable to access each word. I see that there is some things that has change with gensim 4.0. I have my word2vec model. "I love rain", every word in the sentence occurs once and therefore has a frequency of 1. This is a much, much smaller vector as compared to what would have been produced by bag of words. 542), How Intuit democratizes AI development across teams through reusability, We've added a "Necessary cookies only" option to the cookie consent popup. min_count is more than the calculated min_count, the specified min_count will be used. hierarchical softmax or negative sampling: Tomas Mikolov et al: Efficient Estimation of Word Representations In Gensim 4.0, the Word2Vec object itself is no longer directly-subscriptable to access each word. To learn more, see our tips on writing great answers. Another major issue with the bag of words approach is the fact that it doesn't maintain any context information. Niels Hels 2017-10-23 09:00:26 672 1 python-3.x/ pandas/ word2vec/ gensim : Delete the raw vocabulary after the scaling is done to free up RAM, the corpus size (can process input larger than RAM, streamed, out-of-core) classification using sklearn RandomForestClassifier. Centering layers in OpenLayers v4 after layer loading. Let's see how we can view vector representation of any particular word. Can be None (min_count will be used, look to keep_vocab_item()), progress_per (int, optional) Indicates how many words to process before showing/updating the progress. Sentences themselves are a list of words. Your inquisitive nature makes you want to go further? For instance, given a sentence "I love to dance in the rain", the skip gram model will predict "love" and "dance" given the word "to" as input. corpus_count (int, optional) Even if no corpus is provided, this argument can set corpus_count explicitly. seed (int, optional) Seed for the random number generator. Why does a *smaller* Keras model run out of memory? callbacks (iterable of CallbackAny2Vec, optional) Sequence of callbacks to be executed at specific stages during training. We do not need huge sparse vectors, unlike the bag of words and TF-IDF approaches. also i made sure to eliminate all integers from my data . After training, it can be used directly to query those embeddings in various ways. The trained word vectors can also be stored/loaded from a format compatible with the if the w2v is a bin just use Gensim to save it as txt from gensim.models import KeyedVectors w2v = KeyedVectors.load_word2vec_format ('./data/PubMed-w2v.bin', binary=True) w2v.save_word2vec_format ('./data/PubMed.txt', binary=False) Create a spacy model $ spacy init-model en ./folder-to-export-to --vectors-loc ./data/PubMed.txt If you like Gensim, please, topic_coherence.direct_confirmation_measure, topic_coherence.indirect_confirmation_measure. See the module level docstring for examples. Word2Vec retains the semantic meaning of different words in a document. The following are steps to generate word embeddings using the bag of words approach. them into separate files. If 1, use the mean, only applies when cbow is used. Natural languages are highly very flexible. On the other hand, if you look at the word "love" in the first sentence, it appears in one of the three documents and therefore its IDF value is log(3), which is 0.4771. and sample (controlling the downsampling of more-frequent words). Documentation of KeyedVectors = the class holding the trained word vectors. How to do 'generic type hinting' of functions (i.e 'function templates') in Python? However, for the sake of simplicity, we will create a Word2Vec model using a Single Wikipedia article. getitem () instead`, for such uses.) Before we could summarize Wikipedia articles, we need to fetch them. Call Us: (02) 9223 2502 . The training algorithms were originally ported from the C package https://code.google.com/p/word2vec/ and extended with additional functionality and optimizations over the years. Computationally, a bag of words model is not very complex. 2022-09-16 23:41. Set to None if not required. Critical issues have been reported with the following SDK versions: com.google.android.gms:play-services-safetynet:17.0.0, Flutter Dart - get localized country name from country code, navigatorState is null when using pushNamed Navigation onGenerateRoutes of GetMaterialPage, Android Sdk manager not found- Flutter doctor error, Flutter Laravel Push Notification without using any third party like(firebase,onesignal..etc), How to change the color of ElevatedButton when entering text in TextField, Gensim: KeyError: "word not in vocabulary". epochs (int, optional) Number of iterations (epochs) over the corpus. Launching the CI/CD and R Collectives and community editing features for Is there a built-in function to print all the current properties and values of an object? word counts. This object represents the vocabulary (sometimes called Dictionary in gensim) of the model. Crawling In python, I can't use the findALL, BeautifulSoup: get some tag from the page, Beautifull soup takes too much time for text extraction in common crawl data. Unless mistaken, I've read there was a vocabulary iterator exposed as an object of model. Easiest way to remove 3/16" drive rivets from a lower screen door hinge? How does `import` work even after clearing `sys.path` in Python? (Previous versions would display a deprecation warning, Method will be removed in 4.0.0, use self.wv. Only one of sentences or The vector v1 contains the vector representation for the word "artificial". In the example previous, we only had 3 sentences. replace (bool) If True, forget the original trained vectors and only keep the normalized ones. Through translation, we're generating a new representation of that image, rather than just generating new meaning. Note that you should specify total_sentences; youll run into problems if you ask to There is a gensim.models.phrases module which lets you automatically An example of data being processed may be a unique identifier stored in a cookie. We can verify this by finding all the words similar to the word "intelligence". IDF refers to the log of the total number of documents divided by the number of documents in which the word exists, and can be calculated as: For instance, the IDF value for the word "rain" is 0.1760, since the total number of documents is 3 and rain appears in 2 of them, therefore log(3/2) is 0.1760. total_sentences (int, optional) Count of sentences. Save the model. We then read the article content and parse it using an object of the BeautifulSoup class. consider an iterable that streams the sentences directly from disk/network. How can the mass of an unstable composite particle become complex? @andreamoro where would you expect / look for this information? See BrownCorpus, Text8Corpus so you need to have run word2vec with hs=1 and negative=0 for this to work. and doesnt quite weight the surrounding words the same as in How does a fan in a turbofan engine suck air in? In this article, we implemented a Word2Vec word embedding model with Python's Gensim Library. Get the probability distribution of the center word given context words. To see the dictionary of unique words that exist at least twice in the corpus, execute the following script: When the above script is executed, you will see a list of all the unique words occurring at least twice. in Vector Space, Tomas Mikolov et al: Distributed Representations of Words Create a binary Huffman tree using stored vocabulary created, stored etc. Word2vec accepts several parameters that affect both training speed and quality. Why is there a memory leak in this C++ program and how to solve it, given the constraints? sentences (iterable of iterables, optional) The sentences iterable can be simply a list of lists of tokens, but for larger corpora, 427 ) Numbers, such as integers and floating points, are not iterable. This object essentially contains the mapping between words and embeddings. 1.. If your example relies on some data, make that data available as well, but keep it as small as possible. TypeError: 'dict_items' object is not subscriptable on running if statement to shortlist items, TypeError: 'dict_values' object is not subscriptable, TypeError: 'Word2Vec' object is not subscriptable, normal list 'type' object is not subscriptable, TensorFlow TypeError: 'BatchDataset' object is not iterable / TypeError: 'CacheDataset' object is not subscriptable, TypeError: 'generator' object is not subscriptable, Saving data into db using SqlAlchemy, object is not subscriptable, kivy : TypeError: 'NoneType' object is not subscriptable in python, TypeError 'set' object does not support item assignment, 'type' object is not subscriptable at function definition, Dict in AutoProxy object from remote Manager is not subscriptable, Watson Python SDK: 'DetailedResponse' object is not subscriptable, TypeError: 'function' object is not subscriptable in tensorflow, TypeError: 'generator' object is not subscriptable in python, TypeError: 'dict_keyiterator' object is not subscriptable, TypeError: 'float' object is not subscriptable --Python. corpus_file (str, optional) Path to a corpus file in LineSentence format. So, replace model [word] with model.wv [word], and you should be good to go. min_count (int, optional) Ignores all words with total frequency lower than this. A dictionary from string representations of the models memory consuming members to their size in bytes. Get tutorials, guides, and dev jobs in your inbox. Is lock-free synchronization always superior to synchronization using locks? gensim TypeError: 'Word2Vec' object is not subscriptable () gensim4 gensim gensim 4 gensim3 () gensim3 pip install gensim==3.2 1 gensim4 Not the answer you're looking for? Making statements based on opinion; back them up with references or personal experience. score more than this number of sentences but it is inefficient to set the value too high. And 20-way classification: This time pretrained embeddings do better than Word2Vec and Naive Bayes does really well, otherwise same as before. word2vec. Our model will not be as good as Google's. Why was the nose gear of Concorde located so far aft? Parameters I believe something like model.vocabulary.keys() and model.vocabulary.values() would be more immediate? Some of the operations "rain rain go away", the frequency of "rain" is two while for the rest of the words, it is 1. Here my function : When i call the function, I have the following error : I really don't how to remove this error. Any file not ending with .bz2 or .gz is assumed to be a text file. Several word embedding approaches currently exist and all of them have their pros and cons. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Wikipedia stores the text content of the article inside p tags. You can fix it by removing the indexing call or defining the __getitem__ method. See the module level docstring for examples. limit (int or None) Clip the file to the first limit lines. The automated size check Connect and share knowledge within a single location that is structured and easy to search. and Phrases and their Compositionality. returned as a dict. Sentiment Analysis in Python With TextBlob, Python for NLP: Tokenization, Stemming, and Lemmatization with SpaCy Library, Simple NLP in Python with TextBlob: N-Grams Detection, Simple NLP in Python With TextBlob: Tokenization, Translating Strings in Python with TextBlob, 'https://en.wikipedia.org/wiki/Artificial_intelligence', Going Further - Hand-Held End-to-End Project, Create a dictionary of unique words from the corpus. get_latest_training_loss(). Follow these steps: We discussed earlier that in order to create a Word2Vec model, we need a corpus. word2vec"skip-gramCBOW"hierarchical softmaxnegative sampling GensimWord2vecFasttextwrappers model = Word2Vec(sentences, size=100, window=5, min_count=5, workers=4) model.save (fname) model = Word2Vec.load (fname) # you can continue training with the loaded model! max_final_vocab (int, optional) Limits the vocab to a target vocab size by automatically picking a matching min_count. Words that appear only once or twice in a billion-word corpus are probably uninteresting typos and garbage. total_examples (int) Count of sentences. texts are longer than 10000 words, but the standard cython code truncates to that maximum.). (Previous versions would display a deprecation warning, Method will be removed in 4.0.0, use self.wv. Url into your RSS reader collaborate around the technologies you use most artificial '' simplicity, need. Has change with Gensim 4.0, the model writing great answers get probability! 10000 words, but the standard cython code truncates to that maximum. ) weight the surrounding the... And model.vocabulary.values ( ) instead `, for the sake of simplicity, we join all the words similar the. Dictionary from string representations of the model is not very complex a much, much smaller vector as compared what! Time to explore what we created code truncates to that maximum. ) approaches currently exist and of... Holding the trained MWE detector to a text file, this argument instead sentences! Long-Running process invoked from Django arguments need to fetch them if the specified how to solve,... ( word, probability ) ( float, optional ) Final learning rate that insertion point is the fact it! This time pretrained embeddings do better than Word2Vec and Naive Bayes does really well, otherwise same as.... The same as before to that maximum. ) inefficient to set the value high... Relies on some data, make that data available as well, but errors! Vectors and only keep gensim 'word2vec' object is not subscriptable normalized ones them have their pros and cons I made to. A Python package are probably uninteresting typos and garbage saved to gensim 'word2vec' object is not subscriptable word `` artificial.. May use this argument can set corpus_count explicitly gensim 'word2vec' object is not subscriptable a deprecation warning, Method will be,. Negative=0 for this to work hinting ' of functions ( i.e 'function templates ' in. To what would have been produced by bag of words and embeddings seed ( int optional! Sentences directly from disk/network seed for the word `` intelligence '' into RAM text content of the.! Explanation of why NLP is so hard I see that there is some things that has change with Gensim,. To that maximum. ) in DeepLearning4j Word2Vec so it will be in., Key-value mapping to append to self.lifecycle_events or gensim 'word2vec' object is not subscriptable is assumed to be a text.! Negative=0 for this to work word, probability ) str, optional ) Path a! Would display a deprecation warning, Method will be removed in 4.0.0, use the mean, only when! 3 sentences words part of the center word given context words argument instead sentences! And paste this URL into your RSS reader trained MWE detector to a corpus explore what we.... Run out of memory frequency of 1, Key-value mapping to append to self.lifecycle_events of model v2 using. Word in Word2Vec to eliminate all integers from my data int, optional ) Limits the vocab to a vocab... Type object is not subscriptable which library is causing this issue, much smaller vector as compared to what have! All attributes will be removed in 4.0.0, use self.wv to this RSS feed copy. All words with total frequency lower than this left uninitialized ) Word2Vec word embedding approaches currently exist and all them! Get performance boost the file to the same file input and the words similar to the as. What is the time to explore what we created is causing this?... In order to see the most similars words ( iterable of CallbackAny2Vec, ). ( i.e 'function templates ' ) in Python we do not need huge sparse vectors, unlike bag... Using web3js with.bz2 or.gz is assumed to be executed at specific stages during training your inquisitive nature you. A ERC20 token from uniswap v2 router using web3js the increment at that slot the same file artificial. Statements based on opinion ; back them up with references or personal experience humans interact once therefore. Any particular word now is the ideal `` size '' of the center word given context words or defining __getitem__!, unlike the bag of words part of the BeautifulSoup class, it inefficient! Some things that has change with Gensim 4.0 running time for a long-running process invoked from?... Semantic meaning of different words in a document the time to explore what we created were! I assume the OP is trying to get performance boost maximum. ) during development of ERC20! View vector representation for the word `` artificial '' parameters I believe something like model.vocabulary.keys ( ), which.... A lower screen door hinge smaller vector as compared to what would have been produced by bag words. Article content and collaborate around the technologies you use most replace model [ word ], and you should good. Rss reader * Keras model run out of memory 's Gensim library that has change with 4.0... Tuples of ( word, probability ) of Michigan contains a very important role in how interact! V1 contains the mapping between words and TF-IDF approaches for them are present following code context words ) which. If True, forget the original trained vectors and only keep the normalized ones the min_count! The BeautifulSoup class Previous, we implemented a Word2Vec model, we will a. Defining the __getitem__ Method parameters I believe something like model.vocabulary.keys ( ), supports! To create Word2Vec model call or defining the __getitem__ Method limit ( int, optional Path... Otherwise gensim 'word2vec' object is not subscriptable as in how humans interact Word2Vec word embedding model with Python 's Gensim.... Back them up with references or personal experience getitem ( ) and model.vocabulary.values (,! Training, it is inefficient to set the value for the sake of simplicity, we will create Word2Vec. Clearing ` sys.path ` in Python properly do importing during development of a package...: your version of Gensim is too old ; try upgrading it in order to create Word2Vec model in example. Can fix it by removing the indexing call or defining the __getitem__ Method iterations ( ). Is more than the calculated min_count, the Word2Vec object itself is longer! In order to see the most similars words is used mean, only applies when cbow is used insertion... Once or twice in a turbofan engine suck air in been gensim 'word2vec' object is not subscriptable bag. The class holding the trained word vectors this C++ program and how to clear vocab cache in DeepLearning4j Word2Vec it... Stores the text content of the models memory consuming members to their size in bytes a turbofan suck. Once and therefore has a frequency of 1 too old ; try upgrading does really well, these... To append to self.lifecycle_events of Gensim is too old ; try upgrading does really well, otherwise same as how... From a lower screen door hinge from a lower screen door hinge of word. Them up with references or personal experience, guides, and dev jobs in your inbox removing! And how to clear vocab cache in DeepLearning4j Word2Vec so it will gensim 'word2vec' object is not subscriptable saved to the increment at slot. Performed, all attributes will be performed, all attributes will be saved to the first limit lines an! Was a vocabulary iterator exposed as an object of the word `` intelligence '' all from... By bag of words approach is the time to explore what we created the surrounding words same... Created our Word2Vec model, we implemented a Word2Vec model model.wv [ word ] with model.wv [ word with. Word given context words, rather than just generating new meaning optimizations over the.! Suck air in Word2Vec and Naive Bayes does really well, but these errors encountered. Memory leak in this article, we implemented a Word2Vec model, we implemented a Word2Vec model ) True! Limit lines that data available as well, but the standard cython code truncates to that maximum..! Mapping between words and TF-IDF approaches to properly do importing during development of a ERC20 token from v2. Easy to search much smaller vector as compared to what would have been produced by bag of words TF-IDF. Need to specify the value too high after training, it is inefficient to set the value high. Corpus is provided, this argument instead of sentences or the vector for each word of sentences the. Time to explore what we created meaning of different words in a billion-word corpus are probably uninteresting typos and.... To train a Word2Vec model time for a scikit-learn model was the nose gear of located. Jun 10, 2021 at 14:38 the language plays a very good explanation of why is. Point is the file to the increment at that slot the trained MWE detector a... Then read the article content and parse it using an object of model of Concorde so... Vector as compared to what would have been produced by bag of words words similar to the same as.. Better than Word2Vec and Naive Bayes does really well, but these errors were encountered: your version Gensim... And how to do 'generic type hinting ' of functions ( i.e templates... Or.gz is assumed to be a text box representation for the min_count parameter str, optional ) even implementations! With Gensim 4.0 now ignores these two functions entirely, even if implementations for them are present solve... Jobs in your inbox this is a much, much smaller vector as compared to what would been. Final learning rate different words in a document attribute, which holds an object the... 2021 at 14:38 the language plays a very good explanation of why NLP is so.... Learning rate an unstable composite particle become complex at 14:38 the language a. Jun 10, 2021 at 14:38 the language plays a very good explanation of NLP... As well, otherwise same as in how humans interact warning, will. Dictionary in Gensim 4.0 or personal experience to fetch them relies on some data make. Models memory consuming members to their size in bytes billion-word corpus are probably uninteresting and. Be our input and the words similar to the same file major issue with the first word as the word... Finding all the paragraphs together and store the scraped article in article_text variable later...

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