{ "source_file": "0930", "source_platform": "Medium", "author": "Shashank Kapadia", "title_listed": "Practical Introduction to Transformer Models: BERT", "keywords": "", "domain": "文本处理", "content_type": "深度技术解析", "is_paid_flag": "否", "url": "https://towardsdatascience.com/practical-introduction-to-transformer-models-bert-4715ed0deede/", "publish_date": "2023-07-17", "corpus_stage": "", "notes": "", "id": "87b5f48664f9", "collected_at": "2026-10-02T09:54:17", "title": "Practical Introduction to Transformer Models: BERT | Towards Data Science", "body_markdown": "![Revisit consent button](../images/body-df29beacd7496220cc94.svg)\nWe value your privacy\nWe use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. 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It also helps in adverstising on the website.\n\n\n * Cookie\nedgebucket\n * Duration\nsession\n * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n * Cookie\ndid\n * Duration\n1 year\n * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\n# Practical Introduction to Transformer Models: BERT\nHands-on tutorial on how to build your first sentiment analysis model using BERT\n[Shashank Kapadia](https://towardsdatascience.com/author/shashank%20kapadia/)\nJuly 17, 20236 min read\n![A coffee cup with white liquid and a coffee cup with dark liquid on contrasting backgrounds](../images/000.jpg)\nPhoto by Alex Padurariu on Unsplash \n_Preface: This article presents a summary of information about the given topic. It should not be considered original research. The information and code included in this article may have been influenced by things I have read or seen in the past from various online articles, research papers, books, and open-source code._\n## Table of contents\n * [Introduction to BERT](https://towardsdatascience.com/practical-introduction-to-transformer-models-bert-4715ed0deede/#Introduction)\n * [Pre-training and fine-tuning](https://towardsdatascience.com/practical-introduction-to-transformer-models-bert-4715ed0deede/#Pre-training)\n * [Hands-on: Using BERT for sentiment analysis](https://towardsdatascience.com/practical-introduction-to-transformer-models-bert-4715ed0deede/#Hands-on)\n * [Interpreting results](https://towardsdatascience.com/practical-introduction-to-transformer-models-bert-4715ed0deede/#Interpreting-results)\n * [Closing thoughts](https://towardsdatascience.com/practical-introduction-to-transformer-models-bert-4715ed0deede/#Closing)\n\n\nIn NLP, the transformer model architecture has been a revolutionary model architecture that greatly enhanced the ability to understand and generate textual information.\nIn this tutorial, we are going to dig deep into BERT, a well-known transformer-based model, and provide an hands-on example to fine-tune the base BERT model for sentiment analysis.\n## Introduction to BERT\nBERT, introduced by researchers at Google in 2018, is a powerful language model that uses transformer architecture. Pushing the boundaries of earlier model architecture, such as LSTM and GRU, that were either unidirectional or sequentially bi-directional, BERT considers context from both past and future simultaneously. This is due to the innovative “attention mechanism,” which allows the model to weigh the importance of words in a sentence when generating representations.\nLearn this step by step with the interactive [Machine Learning](https://roadmap.sh/machine-learning) roadmap.\nThe BERT model is pre-trained on the following two NLP tasks:\n * Masked Language Model (MLM)\n * Next Sentence Prediction (NSP)\n\n\nand is generally used as the base model for various downstream NLP tasks, such as sentiment analysis, which we will cover in this tutorial.\n## Pre-training and fine-tuning\nThe power of BERT comes from its two-step process:\n * **Pre-training** is the phase where BERT is trained on large amounts of data. As a result, it learns to predict masked words in a sentence (MLM task) and to predict if a sentence follows another one (NSP task). The output of this stage is a pre-trained NLP model with a general-purpose “understanding” of the language\n * **Fine-tuning** is where the pre-trained BERT model is further trained on a specific task. The model is initialized with the pre-trained parameters, and the entire model is trained on a downstream task, allowing BERT to fine-tune its understanding of language to the specifics of the task at hand.\n\n\n## Hands on: Using BERT for sentiment analysis\nThe complete code is available as a [Jupyter Notebook on GitHub](https://github.com/kapadias/medium-articles/blob/master/natural-language-processing/transformers-series/sentiment_analysis_bert.ipynb)\nIn this hands-on exercise, we will train the sentiment analysis model on the IMDB movie reviews dataset [4] [(license: Apache 2.0)](https://github.com/huggingface/datasets/blob/main/LICENSE), which comes labeled whether a review is positive or negative. We will also load the model using the Hugging Face’s transformers library.\nLet’s load all the libraries:\npython\n\n```\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import confusion_matrix, roc_curve, auc\nfrom datasets import load_dataset\nfrom transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer\n\n# Variables to set the number of epochs and samples\nnum_epochs = 10\nnum_samples = 100 # set this to -1 to use all data\n```\n\nFirst, we need to load the dataset and the model tokenizer.\npython\n\n```\n# Step 1: Load dataset and model tokenizer\ndataset = load_dataset('imdb')\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')\n```\n\nNext, we’ll create a plot to see the distribution of the positive and negative classes.\npython\n\n```\n# Data Exploration\ntrain_df = pd.DataFrame(dataset[\"train\"])\nsns.countplot(x='label', data=train_df)\nplt.title('Class distribution')\nplt.show()\n```\n\n![](../images/001.png)\nFig 1. Class distribution of the training dataset\nNext, we preprocess our dataset by tokenizing the texts. We use BERT’s tokenizer, which will convert the text into tokens that correspond to BERT’s vocabulary.\npython\n\n```\n# Step 2: Preprocess the dataset\ndef tokenize_function(examples):\n return tokenizer(examples[\"text\"], padding=\"max_length\", truncation=True)\n\ntokenized_datasets = dataset.map(tokenize_function, batched=True)\n```\n\n![](../images/body-bcee05acf494ff4dd3b1.png)\nAfter that, we prepare our training and evaluation datasets. Remember, if you want to use all the data, you can set the `num_samples` variable to `-1`.\npython\n\n```\nif num_samples == -1:\n small_train_dataset = tokenized_datasets[\"train\"].shuffle(seed=42)\n small_eval_dataset = tokenized_datasets[\"test\"].shuffle(seed=42)\nelse:\n small_train_dataset = tokenized_datasets[\"train\"].shuffle(seed=42).select(range(num_samples)) \n small_eval_dataset = tokenized_datasets[\"test\"].shuffle(seed=42).select(range(num_samples)) \n```\n\nThen, we load the pre-trained BERT model. We’ll use the `AutoModelForSequenceClassification` class, a BERT model designed for classification tasks.\n> For this tutorial, we use the ‘[bert-base-uncased](https://huggingface.co/bert-base-uncased)’ version of BERT, which is trained on lower-case English text, is used for this tutorial.\npython\n\n```\n# Step 3: Load pre-trained model\nmodel = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)\n```\n\nNow, we’re ready to define our training arguments and create a `Trainer`instance to train our model.\npython\n\n```\n# Step 4: Define training arguments\ntraining_args = TrainingArguments(\"test_trainer\", evaluation_strategy=\"epoch\", no_cuda=True, num_train_epochs=num_epochs)\n\n# Step 5: Create Trainer instance and train\ntrainer = Trainer(\n model=model, args=training_args, train_dataset=small_train_dataset, eval_dataset=small_eval_dataset\n)\n\ntrainer.train()\n```\n\n## Interpreting results\nHaving trained our model, let’s evaluate it. We’ll calculate the confusion matrix and the ROC curve to understand how well our model performs.\npython\n\n```\n# Step 6: Evaluation\npredictions = trainer.predict(small_eval_dataset)\n\n# Confusion matrix\ncm = confusion_matrix(small_eval_dataset['label'], predictions.predictions.argmax(-1))\nsns.heatmap(cm, annot=True, fmt='d')\nplt.title('Confusion Matrix')\nplt.show()\n\n# ROC Curve\nfpr, tpr, _ = roc_curve(small_eval_dataset['label'], predictions.predictions[:, 1])\nroc_auc = auc(fpr, tpr)\n\nplt.figure(figsize=(1.618 * 5, 5))\nplt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)\nplt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\nplt.xlim([0.0, 1.0])\nplt.ylim([0.0, 1.05])\nplt.xlabel('False Positive Rate')\nplt.ylabel('True Positive Rate')\nplt.title('Receiver operating characteristic')\nplt.legend(loc=\"lower right\")\nplt.show()\n```\n\n![](../images/body-989eb176bac6c7f40ddc.png)\nFig 2. Confusion Matrix\n![](../images/body-1357b29208e6bfc4b0fb.png)\nFig 3. ROC curve\nThe confusion matrix gives a detailed breakdown of how our predictions measure up to the actual labels, while the ROC curve shows us the trade-off between the true positive rate (sensitivity) and the false positive rate (1 — specificity) at various threshold settings.\nFinally, to see our model in action, let’s use it to infer the sentiment of a sample text.\npython\n\n```\n# Step 7: Inference on a new sample\nsample_text = \"This is a fantastic movie. I really enjoyed it.\"\nsample_inputs = tokenizer(sample_text, padding=\"max_length\", truncation=True, max_length=512, return_tensors=\"pt\")\n\n# Move inputs to device (if GPU available)\nsample_inputs.to(training_args.device)\n\n# Make prediction\npredictions = model(**sample_inputs)\npredicted_class = predictions.logits.argmax(-1).item()\n\nif predicted_class == 1:\n print(\"Positive sentiment\")\nelse:\n print(\"Negative sentiment\")\n```\n\n![](../images/body-37da7d1008175d31e95e.png)\n## Closing thoughts\nBy walking through an example of sentiment analysis on IMDb movie reviews, I hope you’ve gained a clear understanding of how to apply BERT to real-world NLP problems. The Python code I’ve included here can be adjusted and extended to tackle different tasks and datasets, paving the way for even more sophisticated and accurate language models.\n## References\n[1] Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv:1810.04805\n[2] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., … & Polosukhin, I. (2017). Attention is all you need. In Advances in neural information processing systems (pp. 5998–6008).\n[3] Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., … & Rush, A. M. (2019). Huggingface’s transformers: State-of-the-art natural language processing. ArXiv, abs/1910.03771.\n[4] Lhoest, Q., Villanova del Moral, A., Jernite, Y., Thakur, A., von Platen, P., Patil, S., Chaumond, J., Drame, M., Plu, J., Tunstall, L., Davison, J., Šaško, M., Chhablani, G., Malik, B., Brandeis, S., Le Scao, T., Sanh, V., Xu, C., Patry, N., McMillan-Major, A., Schmid, P., Gugger, S., Delangue, C., Matussière, T., Debut, L., Bekman, S., Cistac, P., Goehringer, T., Mustar, V., Lagunas, F., Rush, A., & Wolf, T. (2021). Datasets: A Community Library for Natural Language Processing. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations (pp. 175–184). Online and Punta Cana, Dominican Republic: Association for Computational Linguistics. Retrieved from \nThanks for reading. _If you have any feedback, please feel to reach out by messaging me on_[ _LinkedIn_](https://www.linkedin.com/in/shashankkapadia/) _or shooting me an email (smhkapadia[at]gmail.com)_.\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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It also helps in adverstising on the website.\n\n\n * Cookie\nedgebucket\n * Duration\nsession\n * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n * Cookie\ndid\n * Duration\n1 year\n * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\n# Practical Introduction to Transformer Models: BERT\nHands-on tutorial on how to build your first sentiment analysis model using BERT\n[Shashank Kapadia](https://towardsdatascience.com/author/shashank%20kapadia/)\nJuly 17, 20236 min read\n![A coffee cup with white liquid and a coffee cup with dark liquid on contrasting backgrounds](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/BERT.jpg)\nPhoto by Alex Padurariu on Unsplash \n_Preface: This article presents a summary of information about the given topic. It should not be considered original research. The information and code included in this article may have been influenced by things I have read or seen in the past from various online articles, research papers, books, and open-source code._\n## Table of contents\n * [Introduction to BERT](https://towardsdatascience.com/practical-introduction-to-transformer-models-bert-4715ed0deede/#Introduction)\n * [Pre-training and fine-tuning](https://towardsdatascience.com/practical-introduction-to-transformer-models-bert-4715ed0deede/#Pre-training)\n * [Hands-on: Using BERT for sentiment analysis](https://towardsdatascience.com/practical-introduction-to-transformer-models-bert-4715ed0deede/#Hands-on)\n * [Interpreting results](https://towardsdatascience.com/practical-introduction-to-transformer-models-bert-4715ed0deede/#Interpreting-results)\n * [Closing thoughts](https://towardsdatascience.com/practical-introduction-to-transformer-models-bert-4715ed0deede/#Closing)\n\n\nIn NLP, the transformer model architecture has been a revolutionary model architecture that greatly enhanced the ability to understand and generate textual information.\nIn this tutorial, we are going to dig deep into BERT, a well-known transformer-based model, and provide an hands-on example to fine-tune the base BERT model for sentiment analysis.\n## Introduction to BERT\nBERT, introduced by researchers at Google in 2018, is a powerful language model that uses transformer architecture. Pushing the boundaries of earlier model architecture, such as LSTM and GRU, that were either unidirectional or sequentially bi-directional, BERT considers context from both past and future simultaneously. This is due to the innovative “attention mechanism,” which allows the model to weigh the importance of words in a sentence when generating representations.\nLearn this step by step with the interactive [Machine Learning](https://roadmap.sh/machine-learning) roadmap.\nThe BERT model is pre-trained on the following two NLP tasks:\n * Masked Language Model (MLM)\n * Next Sentence Prediction (NSP)\n\n\nand is generally used as the base model for various downstream NLP tasks, such as sentiment analysis, which we will cover in this tutorial.\n## Pre-training and fine-tuning\nThe power of BERT comes from its two-step process:\n * **Pre-training** is the phase where BERT is trained on large amounts of data. As a result, it learns to predict masked words in a sentence (MLM task) and to predict if a sentence follows another one (NSP task). The output of this stage is a pre-trained NLP model with a general-purpose “understanding” of the language\n * **Fine-tuning** is where the pre-trained BERT model is further trained on a specific task. The model is initialized with the pre-trained parameters, and the entire model is trained on a downstream task, allowing BERT to fine-tune its understanding of language to the specifics of the task at hand.\n\n\n## Hands on: Using BERT for sentiment analysis\nThe complete code is available as a [Jupyter Notebook on GitHub](https://github.com/kapadias/medium-articles/blob/master/natural-language-processing/transformers-series/sentiment_analysis_bert.ipynb)\nIn this hands-on exercise, we will train the sentiment analysis model on the IMDB movie reviews dataset [4] [(license: Apache 2.0)](https://github.com/huggingface/datasets/blob/main/LICENSE), which comes labeled whether a review is positive or negative. We will also load the model using the Hugging Face’s transformers library.\nLet’s load all the libraries:\npython\n\n```\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import confusion_matrix, roc_curve, auc\nfrom datasets import load_dataset\nfrom transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer\n\n# Variables to set the number of epochs and samples\nnum_epochs = 10\nnum_samples = 100 # set this to -1 to use all data\n```\n\nFirst, we need to load the dataset and the model tokenizer.\npython\n\n```\n# Step 1: Load dataset and model tokenizer\ndataset = load_dataset('imdb')\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')\n```\n\nNext, we’ll create a plot to see the distribution of the positive and negative classes.\npython\n\n```\n# Data Exploration\ntrain_df = pd.DataFrame(dataset[\"train\"])\nsns.countplot(x='label', data=train_df)\nplt.title('Class distribution')\nplt.show()\n```\n\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/BERT-1.png)\nFig 1. Class distribution of the training dataset\nNext, we preprocess our dataset by tokenizing the texts. We use BERT’s tokenizer, which will convert the text into tokens that correspond to BERT’s vocabulary.\npython\n\n```\n# Step 2: Preprocess the dataset\ndef tokenize_function(examples):\n return tokenizer(examples[\"text\"], padding=\"max_length\", truncation=True)\n\ntokenized_datasets = dataset.map(tokenize_function, batched=True)\n```\n\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/BERT-2.png)\nAfter that, we prepare our training and evaluation datasets. Remember, if you want to use all the data, you can set the `num_samples` variable to `-1`.\npython\n\n```\nif num_samples == -1:\n small_train_dataset = tokenized_datasets[\"train\"].shuffle(seed=42)\n small_eval_dataset = tokenized_datasets[\"test\"].shuffle(seed=42)\nelse:\n small_train_dataset = tokenized_datasets[\"train\"].shuffle(seed=42).select(range(num_samples)) \n small_eval_dataset = tokenized_datasets[\"test\"].shuffle(seed=42).select(range(num_samples)) \n```\n\nThen, we load the pre-trained BERT model. We’ll use the `AutoModelForSequenceClassification` class, a BERT model designed for classification tasks.\n> For this tutorial, we use the ‘[bert-base-uncased](https://huggingface.co/bert-base-uncased)’ version of BERT, which is trained on lower-case English text, is used for this tutorial.\npython\n\n```\n# Step 3: Load pre-trained model\nmodel = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)\n```\n\nNow, we’re ready to define our training arguments and create a `Trainer`instance to train our model.\npython\n\n```\n# Step 4: Define training arguments\ntraining_args = TrainingArguments(\"test_trainer\", evaluation_strategy=\"epoch\", no_cuda=True, num_train_epochs=num_epochs)\n\n# Step 5: Create Trainer instance and train\ntrainer = Trainer(\n model=model, args=training_args, train_dataset=small_train_dataset, eval_dataset=small_eval_dataset\n)\n\ntrainer.train()\n```\n\n## Interpreting results\nHaving trained our model, let’s evaluate it. We’ll calculate the confusion matrix and the ROC curve to understand how well our model performs.\npython\n\n```\n# Step 6: Evaluation\npredictions = trainer.predict(small_eval_dataset)\n\n# Confusion matrix\ncm = confusion_matrix(small_eval_dataset['label'], predictions.predictions.argmax(-1))\nsns.heatmap(cm, annot=True, fmt='d')\nplt.title('Confusion Matrix')\nplt.show()\n\n# ROC Curve\nfpr, tpr, _ = roc_curve(small_eval_dataset['label'], predictions.predictions[:, 1])\nroc_auc = auc(fpr, tpr)\n\nplt.figure(figsize=(1.618 * 5, 5))\nplt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)\nplt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\nplt.xlim([0.0, 1.0])\nplt.ylim([0.0, 1.05])\nplt.xlabel('False Positive Rate')\nplt.ylabel('True Positive Rate')\nplt.title('Receiver operating characteristic')\nplt.legend(loc=\"lower right\")\nplt.show()\n```\n\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/BERT-3.png)\nFig 2. Confusion Matrix\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/BERT-4.png)\nFig 3. ROC curve\nThe confusion matrix gives a detailed breakdown of how our predictions measure up to the actual labels, while the ROC curve shows us the trade-off between the true positive rate (sensitivity) and the false positive rate (1 — specificity) at various threshold settings.\nFinally, to see our model in action, let’s use it to infer the sentiment of a sample text.\npython\n\n```\n# Step 7: Inference on a new sample\nsample_text = \"This is a fantastic movie. I really enjoyed it.\"\nsample_inputs = tokenizer(sample_text, padding=\"max_length\", truncation=True, max_length=512, return_tensors=\"pt\")\n\n# Move inputs to device (if GPU available)\nsample_inputs.to(training_args.device)\n\n# Make prediction\npredictions = model(**sample_inputs)\npredicted_class = predictions.logits.argmax(-1).item()\n\nif predicted_class == 1:\n print(\"Positive sentiment\")\nelse:\n print(\"Negative sentiment\")\n```\n\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/BERT-5.png)\n## Closing thoughts\nBy walking through an example of sentiment analysis on IMDb movie reviews, I hope you’ve gained a clear understanding of how to apply BERT to real-world NLP problems. The Python code I’ve included here can be adjusted and extended to tackle different tasks and datasets, paving the way for even more sophisticated and accurate language models.\n## References\n[1] Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv:1810.04805\n[2] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., … & Polosukhin, I. (2017). Attention is all you need. In Advances in neural information processing systems (pp. 5998–6008).\n[3] Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., … & Rush, A. M. (2019). Huggingface’s transformers: State-of-the-art natural language processing. ArXiv, abs/1910.03771.\n[4] Lhoest, Q., Villanova del Moral, A., Jernite, Y., Thakur, A., von Platen, P., Patil, S., Chaumond, J., Drame, M., Plu, J., Tunstall, L., Davison, J., Šaško, M., Chhablani, G., Malik, B., Brandeis, S., Le Scao, T., Sanh, V., Xu, C., Patry, N., McMillan-Major, A., Schmid, P., Gugger, S., Delangue, C., Matussière, T., Debut, L., Bekman, S., Cistac, P., Goehringer, T., Mustar, V., Lagunas, F., Rush, A., & Wolf, T. (2021). Datasets: A Community Library for Natural Language Processing. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations (pp. 175–184). Online and Punta Cana, Dominican Republic: Association for Computational Linguistics. Retrieved from \nThanks for reading. _If you have any feedback, please feel to reach out by messaging me on_[ _LinkedIn_](https://www.linkedin.com/in/shashankkapadia/) _or shooting me an email (smhkapadia[at]gmail.com)_.\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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