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<h1>DistilBERT Masked Language Model</h1>

<p>This repository demonstrates how to use Hugging Face Transformers library and TensorFlow to solve a masked language modeling task using DistilBERT. Specifically, we will use the pretrained "distilbert-base-cased" model to predict a missing word in a sentence from the "wikitext-2-raw-v1" dataset.</p>

<h2>1. Problem Statement</h2>

<p>The goal of this project is to predict a missing word in a sentence using the pretrained "distilbert-base-cased" model. The model should take a sentence with a masked token and output the most probable word to fill in the masked token.</p>

<h2>2. Requirements</h2>

<p>Here are the necessary libraries and modules:</p>
<ul>
  <li>Python 3.7+</li>
  <li>TensorFlow 2.0+</li>
  <li>Hugging Face Transformers</li>
  <li>Hugging Face Datasets library</li>
</ul>

<h2>3. Algorithmic Approach</h2>

<p>The algorithmic approach to solving this problem is outlined below:</p>
<ol>
  <li>Import necessary libraries and modules</li>
  <li>Load the pretrained tokenizer and model</li>
  <li>Load the "wikitext-2-raw-v1" dataset and extract the eleventh example from the train split</li>
  <li>Preprocess the text</li>
  <li>Predict the masked token</li>
  <li>Find the most probable token</li>
  <li>Decode the most probable token</li>
  <li>Output the result</li>
</ol>

<h2>4. Usage</h2>

<p>Run the provided Python script to perform masked language modeling with DistilBERT on the given dataset. The script will output the most probable predicted token for the masked position in the sentence.</p>

<h2>5. License</h2>

<p>This project is licensed under the MIT License. See the LICENSE file for more information.</p>

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{"language": ["en"], "license": "mit", "datasets": ["wikitext"]}
en dataset:wikitext license:mit region:us
TAGS
#en #dataset-wikitext #license-mit #region-us 

<!DOCTYPE html>
<html>
<head>
<meta charset="UTF-8">
<title>DistilBERT Masked Language Model - README</title>
</head>
<body>

<h1>DistilBERT Masked Language Model</h1>

<p>This repository demonstrates how to use Hugging Face Transformers library and TensorFlow to solve a masked language modeling task using DistilBERT. Specifically, we will use the pretrained "distilbert-base-cased" model to predict a missing word in a sentence from the "wikitext-2-raw-v1" dataset.</p>

<h2>1. Problem Statement</h2>

<p>The goal of this project is to predict a missing word in a sentence using the pretrained "distilbert-base-cased" model. The model should take a sentence with a masked token and output the most probable word to fill in the masked token.</p>

<h2>2. Requirements</h2>

<p>Here are the necessary libraries and modules:</p>
<ul>
  <li>Python 3.7+</li>
  <li>TensorFlow 2.0+</li>
  <li>Hugging Face Transformers</li>
  <li>Hugging Face Datasets library</li>
</ul>

<h2>3. Algorithmic Approach</h2>

<p>The algorithmic approach to solving this problem is outlined below:</p>
<ol>
  <li>Import necessary libraries and modules</li>
  <li>Load the pretrained tokenizer and model</li>
  <li>Load the "wikitext-2-raw-v1" dataset and extract the eleventh example from the train split</li>
  <li>Preprocess the text</li>
  <li>Predict the masked token</li>
  <li>Find the most probable token</li>
  <li>Decode the most probable token</li>
  <li>Output the result</li>
</ol>

<h2>4. Usage</h2>

<p>Run the provided Python script to perform masked language modeling with DistilBERT on the given dataset. The script will output the most probable predicted token for the masked position in the sentence.</p>

<h2>5. License</h2>

<p>This project is licensed under the MIT License. See the LICENSE file for more information.</p>

</body>
</html>
TAGS
#en #dataset-wikitext #license-mit #region-us 

TAGS
#en #dataset-wikitext #license-mit #region-us 
