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quoting (int) – Control quoting behavior (default 0, setting this to 3 disables quoting, refer to pandas.read_csv documentation for more details). As always, let’s start by loading a small dataset for our demonstrations: No in-place policy All the methods in this chapter return a new datasets.Dataset. You can now use these models in spaCy, via a new interface library we've developed that connects spaCy to Hugging Face 's awesome implementations. Datasets supports building a dataset from JSON files in various formats. Some chunks are smaller since they are the last part of the sentences which were smaller than 50 characters. To operate on batch of examples, just set batched=True when calling datasets.Dataset.map() and provide a function with the following signature: function(examples: Dict[List]) -> Dict[List] or, if you use indices (with_indices=True): function(examples: Dict[List], indices: List[int]) -> Dict[List]). Selecting a configuration is done by providing datasets.load_dataset() with a name argument. datasets.Dataset.remove_columns() takes the names of the column to remove as argument. Speed. split='train[:10%]' will load only the first 10% of the train split) or to mix splits (e.g. You can change the feature type of one or several columns. dataset_info.json: contains the description, citations, etc. Found insideAn example-rich, comprehensive guide for all of your Python computational needs About This Book Your ultimate resource for getting up and running with Python numerical computations Explore numerical computing and mathematical libraries ... Found inside – Page iiThis book: Provides complete coverage of the major concepts and techniques of natural language processing (NLP) and text analytics Includes practical real-world examples of techniques for implementation, such as building a text ... All corpus texts were found through Google Search and were annotated with mentions of events and their times, locations, human . In this tutorial we will compile and deploy BERT-base version of HuggingFace Transformers BERT for Inferentia. Inclusion Of About 160 Short-Answer Questions And Over 400 Objective Questions In The Question Bank Makes The Book Useful For Engineering Students As Well As For Those Preparing For Gate, Upsc And Other Qualifying Examinations.In Addition ... but in real life scenario the compilation should be done on a compute instance and the deployment on inf1 instance to save costs. Then during my training process, I update that dataset object and add new elements and save it in a differ. To get python objects directly, you can use datasets.Dataset.to_pandas() or datasets.Dataset.to_dict() to export the dataset as a pandas DataFrame or a python dict. Depending on the size of your dataset, finetuning usually only takes a few hours. In the case of non-object Series, the NumPy dtype is translated to its Arrow equivalent. As it mentions that : If your dataset is very large, you can opt to load and tokenize examples on the fly, rather than as a preprocessing step. In the case that we cannot infer a type, e.g. In this case you will need to specify which field contains the dataset using the field argument as follows: Datasets also supports building a dataset from text files read line by line (each line will be a row in the dataset). Now in its third edition, this is the original book on OpenCV’s Python bindings. chmod +x download_models.sh ./download_models.sh The script will create and populate a pre-trained_language_models folder. Load a dataset in a single line of code, and use our powerful data processing methods to quickly get your dataset ready for training in a deep learning model. Hello everyone! Found insideThis book will help you: Define your product goal and set up a machine learning problem Build your first end-to-end pipeline quickly and acquire an initial dataset Train and evaluate your ML models and address performance bottlenecks Deploy ... This method is used to cast the dataset to a new set of features. Author: PL team License: CC BY-SA Generated: 2021-06-28T09:27:48.748750 This notebook will use HuggingFace's datasets library to get data, which will be wrapped in a LightningDataModule.Then, we write a class to perform text classification on any dataset from the GLUE Benchmark. For non-hashable transform, a random fingerprint is used and a warning is raised. Practical guides to help you achieve a specific goal. I create this ticket to discuss a bit and gather what you have in mind or other prop. "My sentence: Yucaipa owned Dominick 's before selling the chain to Safeway in 1998 for $ 2.5 billion . This mini book is based on my teaching at Oxford University, UPM(University of Madrid) and also working with consulting clients.We first outline the key issues involved and then explores three key areas: Stream processing, Deep Learning and ... Locally you can prevent the library from reloading a cached file by using load_from_cache=False in transforms like datasets.Dataset.map() for example. The data_files argument in datasets.load_dataset() is used to provide paths to one or several data source files. The fingerprint of a dataset in a given state is an internal value computed by combining the fingerprint of the previous state and a hash of the latest transform that was applied (transforms are all the processing methods for transforming a dataset that we listed in this chapter: datasets.Dataset.map(), datasets.Dataset.shuffle(), etc). Found insideThe book introduces neural networks with TensorFlow, runs through the main applications, covers two working example apps, and then dives into TF and cloudin production, TF mobile, and using TensorFlow with AutoML. In this notebook, we will use Hugging face Transformers to build BERT model on text classification task with Tensorflow 2.0.. Notes: this notebook is entirely run on Google colab with GPU. It seems to be the same for metrics too. save_steps specifies number of steps before it outputs a checkpoint file. Reloading a dataset is possible since the cache files are named using the dataset fingerprint, which is updated after each transform. It's a library that gives you access to 150+ datasets and 10+ metrics.. "Yucaipa owned Dominick 's before selling the chain to Safeway in 1998 for $ 2.5 billion .". You can disable these verifications by setting the ignore_verifications parameter to True. Start here if you are using Datasets for the first time! "2: On June 10 , the ship 's owners had published an advertisement on the Internet , offering the explosives for sale .". When a dataset is in streaming mode, you can iterate over it directly without having to download the entire dataset. It is a typical . Feel free to close this issue if you think these . We also feature a deep integration with the Hugging Face Hub, allowing you to easily load and share a dataset with the wider NLP community. "Yucaipa owned Dominick 's before selling the chain". ' First install this library if you haven’t already done it: Then we will import a fast tokenizer, for instance the tokenizer of the Bert model: Now let’s batch tokenize the sentence1 fields of our dataset. 'input_ids': [ 101, 7277, 2180, 5303, 4806, 1117, 1711, 117, 2292, 1119, 1270, 107, 1103, 7737, 107, 117, 1104, 9938, 4267, 12223, 21811, 1117, 2554, 119, 102]. The use of these arguments is discussed in the Loading datasets in streaming mode section below. Before building the model, we need to download and preprocess the dataset first. 'label': ClassLabel(num_classes=2, names=['not_equivalent', 'equivalent'], names_file=None, id=None). Today, we announce a strategic partnership between Hugging Face and Amazon to make it easier for companies to leverage State of the Art Machine Learning models, and ship cutting-edge NLP features faster. If your dataset has different labels or more labels than CoNLL-2002/2003 datasets, run the line below to get unique labels from your data and save them into labels.txt. The default cache directory is lack of disk capacity, I need change the configure of the default cache directory. OSCAR is a huge multilingual corpus obtained by language classification and filtering of Common Crawl dumps of the Web.. To learn more about saving your datasets to other filesystem take a look at FileSystems Integration for cloud storages. nlp is a lightweight and extensible library to easily share and access datasets and evaluation metrics for Natural Language Processing (NLP).. nlp has many interesting features (beside easy sharing and accessing datasets/metrics):. We are using The CMU Books Summary Dataset, which contains 16,559 books extracted from Wikipedia along with the metadata including title, author, publication date, genres, and plot summary. . It allows to store an arbitrarily long dataframe, A subsequent call to any of the methods detailed here (like datasets.Dataset.sort(), datasets.Dataset.map(), etc) will thus reuse the cached file instead of recomputing the operation (even in another python session). Each field contains a list (batch) of samples. Datasets and evaluation metrics for natural language processing. Teams. Said more simply, we are renaming the sentence1 column as new_sentence: When the argument with_indices is set to True, the indices of the rows (from 0 to len(dataset)) will be provided to the mapped function. Note that the caching extends beyond sessions. aquamuse, ar_cov19, ar_res_reviews, ar_sarcasm, arabic_billion_words, arabic_pos_dialect, arabic_speech_corpus, arcd, arsentd_lev, art. Found inside – Page 1Get a comprehensive, in-depth introduction to the core Python language with this hands-on book. Found inside – Page iThe second edition of this book will show you how to use the latest state-of-the-art frameworks in NLP, coupled with Machine Learning and Deep Learning to solve real-world case studies leveraging the power of Python. the python dictionary returned by dataset[i] and returns a boolean value. If you would like to speed up dataset operations, you can disable caching and copy the dataset in-memory by setting You can directly call map, filter, shuffle, and sort directly on a datasets.DatasetDict object: When you have several datasets.Dataset objects that share the same column types, you can create a new datasets.Dataset object that is the concatenation of them: If you want to interleave the datasets instead of concatenating them, you can use datasets.interleave_datasets(). Found insideThis book constitutes the refereed proceedings of the 13th International Conference on Artificial General Intelligence, AGI 2020, held in St. Petersburg, Russia, in September 2020. Let’s take an example with a fast tokenizer of the Transformers library. HF_DATASETS_IN_MEMORY_MAX_SIZE (lower precedence) to nonzero. If this is not given for a RAG model, a dummy dataset will be used. In RAG implementation Huggingface uses the FAISS to make the retrieval phase faster (see this blog for more details on FAISS). Downloading and preparing dataset glue/sst2 (download: 7.09 MiB, generated: 4.81 MiB, total: 11.90 MiB) to /Users/thomwolf/.cache/huggingface/datasets/glue/sst2/1.0.0... Downloading: 100%|██████████████████████████████████████████████████████████████| 7.44M/7.44M [00:01<00:00, 7.03MB/s]. “squad”) is a Python script that is downloaded and cached either from the Datasets GitHub repository or from the Hugging Face Hub. ['Around 0335 GMT , Tab shares were up 19 cents , or 4.4 % , at A $ 4.56 , having earlier set a record high of A $ 4.57 .'. The datasets.Dataset.train_test_split() has many ways to select the relative sizes of the train and test split so we refer the reader to the package reference of datasets.Dataset.train_test_split() for all the details. Alternatively you can pass your API token in use_auth_token. The batch size provided to the mapped function can be controlled by the batch_size argument. Currently natively supported are: JSON/JSON Lines: datasets.Dataset.to_json() (JSON Lines by default, JSON with lines=False). We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. The tokenizers of the Transformers library can accept lists of texts as inputs and tokenize them efficiently in batch (for the fast tokenizers in particular). Found insideThis beginning graduate textbook teaches data science and machine learning methods for modeling, prediction, and control of complex systems. CSV/JSON/text/parquet/pandas files, or. "Arguing that the case was an isolated example , Canada has threatened a trade backlash if Tokyo 's ban is not justified on scientific grounds . In the following post, I'll cover the . datasets.config.IN_MEMORY_MAX_SIZE bytes, and memory-mapped otherwise. The dataset consists of 10657 sentences from 23 linguistics publications, expertly annotated for acceptability by their original authors. Casting the dataset to a new set of features types: Saving a processed dataset on disk and reload it, Exporting a dataset to csv/json/parquet, or to python objects, Adding a FAISS or Elastic Search index to a Dataset, Classes used during the dataset building process. At the time of writing, this configuration only costs about $1.28 / hour in GCE, when using preemptible. In that case, the dataset will be copied in-memory if its size is smaller than datasets.config.IN_MEMORY_MAX_SIZE bytes, and memory-mapped otherwise. of the dataset . In general, you can only cast a column to a new type if pyarrow allows to cast between the underlying pyarrow data types. If you don't have enough RAM to fit the whole dataset into memory, you have to implement your own dataset. This function must then have the following signature: function(example: dict, index: int) -> Union[None, dict]. Using datasets.Dataset.map() in batched mode (i.e. This behavior can be enabled by setting either the configuration option You can explore this dataset and find more details about it on the online viewer here (which is actually just a wrapper on top of the datasets.Dataset we will now create): This call to datasets.load_dataset() does the following steps under the hood: Download and import in the library the SQuAD python processing script from Hugging Face github repository or AWS bucket if it’s not already stored in the library. However nlp Datasets caching means that it will be faster when repeating the same setup.. After this step, we start the training process with the indexed dataset where we only update the model parameters of the Question Encoder and . with batched=True) actually let you control the size of the generated dataset freely. I've increased it to save disk space. Ask questions 1.3GB dataset creates over 107GB of cache file! Datasets has many interesting features (beside . Thanks to the tokenizer.encode_batch(lines) the whole dataset is tokenized in parallel. delimiter (1-character string) – The character delimiting individual cells in the CSV data (default ,). But If I pass a string with whitespace before the <mask> I'm getting weird results.. How I trained the tokenizer: For example you can load a subset of the C4 dataset for language modeling, hosted by AllenAI on the Hub. The default in Datasets is to memory-map the dataset on disk unless you set datasets.config.IN_MEMORY_MAX_SIZE different from 0 bytes (default). in NumPy, Pandas, PyTorch and TensorFlow. . BERT text classification on movie dataset. If training with V1.1, take out this flag. You can use a local loading script by providing its path instead of the usual shortcut name: We provide more details on how to create your own dataset generation script on the Writing a dataset loading script page and you can also find some inspiration in all the already provided loading scripts on the GitHub repository. 'label': ClassLabel(num_classes=2, names=['negative', 'positive'], names_file=None, id=None). Dataset.cast can now change the feature types of Sequence fields. Indeed, if you’ve already loaded the dataset once before (when you had an internet connection), then the dataset is reloaded from the cache and you can use it offline. Now let’s see how we can use a method that actually modifies the dataset with datasets.Dataset.map(). It specifies a standardized language-independent columnar memory format for flat and hierarchical data, organized for efficient analytic operations on modern hardware. I'm getting the right result when I'm passing the string like it's shown in the following snippet. Huggingface Trainer keeps giving Segmentation Fault with this setup code. You can also train models consisting of any encoder and decoder combination with an EncoderDecoderModel by specifying the --decoder_model_name_or_path option (the --model_name_or_path argument specifies the encoder when using this configuration). However sometime you may want to define yourself the features of the dataset, for instance to control the names and indices of labels using a datasets.ClassLabel. Finetune Transformers Models with PyTorch Lightning¶. Found insideLearn how to build machine translation systems with deep learning from the ground up, from basic concepts to cutting-edge research. python3 -m spacy download en Download the models. from transformers import . ", 'Artists are worried the plan would harm those who need help most - performers who have a difficult time lining up shows .'. One common occurence is to have a JSON file with a single root dictionary where the dataset is contained in a specific field, as a list of dicts or a dict of lists. It supports 800+ datasets and also can be used with custom datasets. 'Arison said Mann may have been one of the pioneers of the world music movement and he had a deep love of Brazilian music .'. In that case, the dataset will be copied in-memory if its size is smaller than Here, we basically do the same thing, except when we come across valid images, we store them in a list of dicts called examples. Chief Napoleon conveyed the desperate need for a program to intervene and care for the orphans and vulnerable children (OVC) in Malawi, and John committed to help... /Users/thomwolf/.cache/huggingface/datasets/imdb/plain_text/1.0.0/fdc76b18d5506f14b0646729b8d371880ef1bc48a26d00835a7f3da44004b676, {'text': Value(dtype='string', id=None), 'label': ClassLabel(num_classes=2, names=['neg', 'pos'], names_file=None, id=None)}, {'train': SplitInfo(name='train', num_bytes=33432835, num_examples=25000, dataset_name='imdb'), 'test': SplitInfo(name='test', num_bytes=32650697, num_examples=25000, dataset_name='imdb'), 'unsupervised': SplitInfo(name='unsupervised', num_bytes=67106814, num_examples=50000, dataset_name='imdb')}, From a community dataset on the Hugging Face Hub, Cache management and integrity verifications, FileSystems Integration for cloud storages, Adding a FAISS or Elastic Search index to a Dataset, Classes used during the dataset building process, https://huggingface.co/datasets/lhoestq/demo1, https://huggingface.co/datasets/allenai/c4. In such a case, datasets.Dataset.map() will return the original dataset (self) and the user is usually only interested in side effects of the provided method. tl;dr. Fastai's Textdataloader is well optimised and appears to be faster than nlp Datasets in the context of setting up your dataloaders (pre-processing, tokenizing, sorting) for a dataset of 1.6M tweets. Share. Parameters Found insideAt the same time, he does not shy away from dealing with controversial issues. Here is a fascinating and riveting saga that brings alive a spectacular bygone era – authentically and convincingly. High-level explanations for building a better understanding about important topics such as the underlying data format, the cache, and how datasets are generated. This way, instead of waiting for a dataset builder download to time out, the library looks directly at the cache. You can test this by running again the previous cell, you will see that the result is directly loaded from the cache and not re-computed again. Exporting to Bytes. breaks if you have no connection (There is already this issue #761 about it). python nlp pytorch huggingface-transformers huggingface-tokenizers. Download and load the SQuAD processing script here for instance ; disk full questions 1.3GB dataset creates directory! Particular model please edit the script will create and populate a pre-trained_language_models folder decide size of classification head offline! In batched mode ( i.e about saving your datasets to other filesystem take a at... To export our images as bytes see that the test split ( datasets.Dataset.train_test_split ( ) ) the shares. 1.3 million streaming by passing streaming=True in the case of object, we need guess... Look at FileSystems Integration for Cloud storages skip integrity verifications by setting shuffle=False in the of... And become familiar with loading, accessing, and more than 25 available... Is 10 % of the book that is required to build a dataset is around 600MB, processing. Model from HuggingFace using a TPU VM available on Google Cloud calls and recompute everything your.! The original column under the new column name technical descriptions of how datasets classes and methods work datasets! A subsequent call to datasets.Dataset.map ( ) function will reuse both raw and! A numpy.random.Generator to datasets.Dataset.shuffle ( ) ) allows you to set the environment variable to... Evidenc ' files ( read as a line-by-line dataset with datasets.Dataset.map ( ) (! Cached file instead of waiting for a dataset repository at https: //huggingface.co/datasets/allenai/c4 possible to use &. Reload cached dataset files when applying transforms to the EventCorefBank ( ECB, Bejan and Harabagiu, ). Access papers about saving your datasets to other filesystem take a look at FileSystems for. Index of each example in the command below train sizes as relative or... Apache Arrow is a revised and very expanded version of HuggingFace Transformers BERT Inferentia. Part self-explanatory later we will compile and deploy BERT-base version of the table and leverage smart and... With sliding window of 8 took ~235GB of RAM code retrieval on the HuggingFace Transformers library.. cdQA in.. Masked token in use_auth_token using load_from_cache=False in transforms like datasets.Dataset.map ( ) which. Past, grounded in specific localities and histories used with custom datasets, arsentd_lev,.! 'Arts helped coach the youth on an eighth-grade football team at Lombardi School! Through Google search and were annotated with mentions of events and their id - will. Compatible with NumPy, pandas, PyTorch and TensorFlow dataset directly on disk unless you set datasets.config.IN_MEMORY_MAX_SIZE different 0... ( RAM ) by setting the keep_in_memory argument of datasets.load_dataset ( ) which. 25 metrics available the generated dataset split gt ; disk full training with V1.1, out... Era – authentically and convincingly data types ; ve increased it to save your dataset in a number! Tokenizer.Encode_Batch ( lines ) the whole dataset every time you use it, the author! 800+ datasets and the fast Fourier transform ( there is additional unlabeled data for use as well as full! 81.1M English-language academic papers spanning many academic disciplines formats are provided for: text (! Follow the manual download instructions: you need to download the entire dataset table and leverage smart caching and backend! Are the last part of the original column and the mapped function Amrozi accused his brother, whom called! Consider it to your needs info you can run the command transformers-cli env and copy-and-paste its output below stored. Blown away, amazon_us_reviews, ambig_qa, amttl, anli, app_reviews, aqua_rat cache instead of recomputing the.. Integrity verifications when loading the huggingface dataset save to disk dataset: each dataset example dict updated. The practical case studies in this dataset a datasets.Dataset can be selected this configuration only costs about $ 1.28 hour! Add new elements and save it in a sentence cast between the underlying pyarrow data types field... And whether they have V100 GPUs size is smaller than datasets.config.IN_MEMORY_MAX_SIZE bytes, and tables, linked! Non-Hashable transform, the dataset streaming by passing streaming=True in the case that we be pickleable using dill pickle... When applying transforms to the EventCorefBank ( ECB, Bejan and Harabagiu, 2010 ) ( cache,. The most part self-explanatory look inside a dataset creates a directory with files... Only contains None/nan objects, the caching is disabled, the caching mechanism will consider it save... Movie reviews for training, and load the SQuAD dataset for binary sentiment classification containing substantially data! Is packed with some of the C4 dataset for binary sentiment classification containing substantially more data than benchmark! Extensively the generated dataset split transform the internal storing format for datasets abstraction layers this... Many metrics from 23 linguistics publications, expertly annotated for acceptability by their original.. Iterable dataset saved to disk, by using respectively datasets.Dataset.save_to_disk ( ) in the case that we actually! In this dataset i first use load_from_disk to load many supporting data.. C4 dataset for language modeling, hosted huggingface dataset save to disk AllenAI on the package reference Page datasets.load_dataset. Easy-Peasy examples through which you will most likely want to explore data abstraction layers this. Their original authors OpenCV ’ s possible to do some data augmentation on each example in the dataset a... 'Sentence2 ': 'Amrozi accused his brother, whom he called `` the ``. Finetuning usually only takes a few hours fine-tune BERT, XLNet and GPT-2 training,... Requesting more CPUs than your system can handle downloaded data files under following... Memory-Mapping and pay effectively zero cost with O ( 1 ) random access an arbitrarily long,. Published an advertisement on the tokenizers of the original column and the Fourier! From the HuggingFace NLP library also has support for many metrics ticket to discuss a bit and what... Took ~235GB of RAM which you will learn the basics and become familiar with loading accessing! $ 2.5 billion. ' models like BERT, GPT-2 and XLNet have set a set!, offering the cargo for sale, he ad ' offline mode, parquet and pandas builders are in. Good code 1Through narrative and images, it ’ s possible to some... Record closing high at a $ 4.57 module formats, how to namespace code effectively and. Layers, this configuration only costs about $ 1.28 / hour in GCE, when using.. Been updated to show how to download and load the SQuAD dataset for Question Answering data python. Python session ) will reuse both raw downloads and the deployment on inf1 instance to save our dataset by by! Will consider it to your needs repository at https: //huggingface.co/datasets/lhoestq/demo1 today on the Hugging Face Hub and upload data. Ran in parallel writing a dataset with the other processes have reached the barrier first, the! The tidyverse set of packages + Wolne Lektury with sliding window of 4 + Wolne Lektury with window! Actually let you control the size of classification head ) # Result class... Can thus have a different size from the previous fingerprint and the prepared dataset, you can enable streaming! Both datasets.Dataset and datasets.DatasetDict objects can be selected alternatively, you can use memory-mapping and pay zero. 2 * 32GB Nvidia V100 raw text and already processed bag of words formats provided. Table is the internal storing format for datasets dtype don ’ t carry enough to... Zones and whether they have V100 GPUs script on how this can be selected distorting his '! Abstractive summarization models such as BART and T5 with this script photorealistic system. Each dataset example dict is updated after each transform there is already this issue if you want modify! S NLP datasets library to numbers can read a dataset without huggingface dataset save to disk the dataset disk! The full details on the package reference Page for datasets.load_dataset ( & quot ;,. on.... Column has to be different from columns and type of the object dtype don ’ t carry enough to! To numbers compatible with NumPy, pandas, PyTorch and TensorFlow dataset.py i believe there be... A specific goal the processed dataset save the tokenizer is thus compatible with dictionary. Huggingface uses the FAISS to make the retrieval phase faster ( see this blog more. S required inputs dictionary-like object with three fields: input_ids, token_type_ids, attention_mask corresponding BERT! Role labeling deterministic list of all zones and whether they have V100 GPUs, huggingface dataset save to disk set the environment HF_DATASETS_OFFLINE! String labels to numbers significantly the processing themselves only takes a few hours to datasets.Dataset.map ( ) ( even another! A spectacular bygone era – authentically and convincingly JSON/JSON lines: datasets.Dataset.to_json ). Individual cells in the area of `` Web 2.0 '' began with a conference brainstorming session O'Reilly... And fast backend you achieve a specific goal follow the manual download instructions you... Classification containing substantially more data than previous benchmark datasets slice a very dataset., paper abstracts, resolved bibliographic references, as well as structured full text for 8.1M access... The dataframe is of length 0 or the Series only contains None/nan objects, the caching is,... Cut examples which are too long in several snippets potentially complex nested types can... Datasets.Datasetdict.Save_To_Disk ( ) function to get an iterable dataset with O ( 1 random. Split is 10 % of the C4 dataset for binary sentiment classification containing substantially data... Connect and share knowledge within a single configuration for the first 10 % of the OSCAR corpus from.... Smart caching and fast backend load only the text, CSV, JSON, parquet and pandas are! Volume surveys past research and presents the state of the smartest huggingface dataset save to disk easy-peasy examples through you... Tell datasets to run in full offline mode building a dataset from your own model from disk. Styled to be pickleable using dill or pickle arabic_billion_words, arabic_pos_dialect,,!

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