title
Tutorial 1-Transformer And Bert Implementation With Huggingface
description
google colab link
https://colab.research.google.com/drive/1xyaAMav_gTo_KvpHrO05zWFhmUaILfEd?usp=sharing
đ¤ Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNetâŚ) for Natural Language Understanding (NLU) and Natural Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.
-----------------------------------------------------------------------------------------------------------------------
â Kite is a free AI-powered coding assistant that will help you code faster and smarter. The Kite plugin integrates with all the top editors and IDEs to give you smart completions and documentation while youâre typing. I've been using Kite for a few months and I love it! https://www.kite.com/get-kite/?utm_medium=referral&utm_source=youtube&utm_campaign=krishnaik&utm_content=description-only
Subscribe my vlogging channel
https://www.youtube.com/channel/UCjWY5hREA6FFYrthD0rZNIw
Please donate if you want to support the channel through GPay UPID,
Gpay: krishnaik06@okicici
Telegram link: https://t.me/joinchat/N77M7xRvYUd403DgfE4TWw
Please join as a member in my channel to get additional benefits like materials in Data Science, live streaming for Members and many more
https://www.youtube.com/channel/UCNU_lfiiWBdtULKOw6X0Dig/join
Connect with me here:
Twitter: https://twitter.com/Krishnaik06
Facebook: https://www.facebook.com/krishnaik06
instagram: https://www.instagram.com/krishnaik06
detail
{'title': 'Tutorial 1-Transformer And Bert Implementation With Huggingface', 'heatmap': [{'end': 1073.986, 'start': 1029.388, 'weight': 1}], 'summary': 'Tutorial covers nlp tasks implementation using huggingface library, focusing on bert model, sentiment analysis, multilingual sentiment analysis with distilbert, nlp model training, and the tokenization process, achieving 91.3% accuracy on sst2 devset and an accuracy level of four for the sentiment analysis model.', 'chapters': [{'end': 182.872, 'segs': [{'end': 111.507, 'src': 'embed', 'start': 74.101, 'weight': 0, 'content': [{'end': 80.365, 'text': 'And for entirely for this particular implementation, we are going to use HuggingFace libraries, Transformers.', 'start': 74.101, 'duration': 6.264}, {'end': 81.026, 'text': 'So over here.', 'start': 80.405, 'duration': 0.621}, {'end': 81.846, 'text': 'what is HuggingFace?', 'start': 81.026, 'duration': 0.82}, {'end': 89.312, 'text': 'See guys, HuggingFace is the NLP focused startup with a large open source community in particularly around the Transformer library.', 'start': 81.866, 'duration': 7.446}, {'end': 96.056, 'text': 'So Transformer is a Python based library that exposes an API to many well-known Transformer architecture.', 'start': 89.832, 'duration': 6.224}, {'end': 100.66, 'text': 'such as BERT, Robota, GPT-2 or Distilbert.', 'start': 96.977, 'duration': 3.683}, {'end': 107.704, 'text': 'that obtains the state of art results on variety of NLP tasks like classification, information extraction, question answering and test generation.', 'start': 100.66, 'duration': 7.044}, {'end': 111.507, 'text': 'So this particular library that is Transformer.', 'start': 108.185, 'duration': 3.322}], 'summary': "Huggingface's transformers library includes bert, robota, gpt-2, and distilbert, achieving state-of-the-art nlp results.", 'duration': 37.406, 'max_score': 74.101, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/DkzbCJtFvqM/pics/DkzbCJtFvqM74101.jpg'}], 'start': 13.953, 'title': 'Nlp with transformers implementation', 'summary': 'Discusses the implementation of transformers for nlp tasks using the huggingface library, emphasizing the bert model and its usage for various nlp tasks, showcasing its wide applicability and potential for transfer learning.', 'chapters': [{'end': 182.872, 'start': 13.953, 'title': 'Nlp with transformers implementation', 'summary': 'Discusses the implementation of transformers for nlp tasks using the huggingface library, highlighting its wide applicability and the potential for transfer learning, with an emphasis on the bert model and its usage for various nlp tasks.', 'duration': 168.919, 'highlights': ['Transformers library by HuggingFace provides state-of-the-art algorithms for NLP tasks like text classification, question answering, and text summarization, with support for BERT, GPT-2, and Distilbert, enabling transfer learning for specific datasets.', 'The implementation of Transformers for NLP tasks using the HuggingFace library is the focus of the discussion, with the potential for transfer learning and fine-tuning models for specific use cases, offering a wide range of applications for companies in various industries.', 'The importance of the BERT model and its usage for various NLP tasks is emphasized, with a recommendation to refer to a specific article or blog by Jay Alamar for in-depth understanding of the BERT architecture.']}], 'duration': 168.919, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/DkzbCJtFvqM/pics/DkzbCJtFvqM13953.jpg', 'highlights': ['Transformers library by HuggingFace provides state-of-the-art algorithms for NLP tasks with support for BERT, GPT-2, and Distilbert', 'The implementation of Transformers for NLP tasks using the HuggingFace library enables transfer learning for specific datasets', 'The importance of the BERT model and its usage for various NLP tasks is emphasized']}, {'end': 446.183, 'segs': [{'end': 243.52, 'src': 'embed', 'start': 183.313, 'weight': 0, 'content': [{'end': 185.974, 'text': "So you'll be learning many things along with me.", 'start': 183.313, 'duration': 2.661}, {'end': 193.402, 'text': 'And probably I have also not learned it completely to its depth, but this will give me an opportunity to learn and then probably upload a video.', 'start': 186.514, 'duration': 6.888}, {'end': 194.363, 'text': "So let's go ahead.", 'start': 193.682, 'duration': 0.681}, {'end': 196.526, 'text': "I'll just go in to connect to my runtime.", 'start': 194.543, 'duration': 1.983}, {'end': 198.388, 'text': "First of all, let's change the runtime.", 'start': 196.566, 'duration': 1.822}, {'end': 200.25, 'text': "I'm just going to make it as GPU.", 'start': 198.808, 'duration': 1.442}, {'end': 205.692, 'text': "Okay, and probably I want high RAM because I'm using Google Colab Pro.", 'start': 201.331, 'duration': 4.361}, {'end': 209.613, 'text': "Okay, so this will get initialized and let's start how we are.", 'start': 206.072, 'duration': 3.541}, {'end': 219.935, 'text': "We will basically start what are the main things with respect to transformers, how it is able to do all this kind of task we'll try to understand.", 'start': 209.933, 'duration': 10.002}, {'end': 222.995, 'text': "But today we'll just try to see the power of transformers.", 'start': 220.295, 'duration': 2.7}, {'end': 226.856, 'text': "Different, different tasks we'll try to see what transformers can actually solve.", 'start': 223.455, 'duration': 3.401}, {'end': 227.736, 'text': "So let's go.", 'start': 227.256, 'duration': 0.48}, {'end': 229.697, 'text': 'So the runtime is ready.', 'start': 228.296, 'duration': 1.401}, {'end': 232.817, 'text': 'Now I am first of all going to pip install transformers.', 'start': 230.137, 'duration': 2.68}, {'end': 235.618, 'text': 'This is how you have to do the installation with respect to transformers.', 'start': 232.837, 'duration': 2.781}, {'end': 237.038, 'text': "So I'll just execute it.", 'start': 235.958, 'duration': 1.08}, {'end': 242.019, 'text': "So I'll give you this entire Google collab notebook probably in the description.", 'start': 237.678, 'duration': 4.341}, {'end': 243.52, 'text': 'You can definitely check it out.', 'start': 242.379, 'duration': 1.141}], 'summary': 'Learning about transformers and google colab pro for nlp tasks.', 'duration': 60.207, 'max_score': 183.313, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/DkzbCJtFvqM/pics/DkzbCJtFvqM183313.jpg'}, {'end': 283.93, 'src': 'embed', 'start': 260.432, 'weight': 1, 'content': [{'end': 269.24, 'text': 'Okay, the library downloads pre-trained models for natural language understanding, that is NLU task, such as analyzing the sentiments of a text.', 'start': 260.432, 'duration': 8.808}, {'end': 274.705, 'text': 'natural language generation, such as completion or prompt with new text or translating into another language.', 'start': 269.24, 'duration': 5.465}, {'end': 281.128, 'text': 'so translation is also there, sentiment analysis is also there, completing a sentence is also there.', 'start': 275.646, 'duration': 5.482}, {'end': 283.93, 'text': 'so sentence generation is also there, and there are many things.', 'start': 281.128, 'duration': 2.802}], 'summary': 'Library downloads pre-trained models for nlu tasks like sentiment analysis, translation, and sentence generation.', 'duration': 23.498, 'max_score': 260.432, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/DkzbCJtFvqM/pics/DkzbCJtFvqM260432.jpg'}, {'end': 344.294, 'src': 'embed', 'start': 317.447, 'weight': 3, 'content': [{'end': 323.208, 'text': 'see over here some of the use cases that has been mentioned, like sentiment analysis, whether a text is positive or negative.', 'start': 317.447, 'duration': 5.761}, {'end': 326.489, 'text': 'text generation in English name entity recognition.', 'start': 323.208, 'duration': 3.281}, {'end': 328.79, 'text': 'question answering field masters summarization.', 'start': 326.489, 'duration': 2.301}, {'end': 330.13, 'text': 'translation feature extraction.', 'start': 328.79, 'duration': 1.34}, {'end': 335.151, 'text': 'Now see how this thing is implemented and how simple it is.', 'start': 331.17, 'duration': 3.981}, {'end': 339.292, 'text': 'So over here, first of all, you import from transformers, you import pipeline.', 'start': 335.731, 'duration': 3.561}, {'end': 344.294, 'text': 'Whenever you import pipeline, guys, two things gets loaded okay?', 'start': 339.312, 'duration': 4.982}], 'summary': 'Use cases include sentiment analysis, text generation, name entity recognition, question answering, summarization, translation, and feature extraction using transformers and pipeline.', 'duration': 26.847, 'max_score': 317.447, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/DkzbCJtFvqM/pics/DkzbCJtFvqM317447.jpg'}, {'end': 385.008, 'src': 'embed', 'start': 356.523, 'weight': 4, 'content': [{'end': 360.607, 'text': 'Now this pipeline helps me to call a pre-trained model okay?', 'start': 356.523, 'duration': 4.084}, {'end': 365.991, 'text': 'That pre-trained model may be with respect to a BERT architecture, maybe with respect to a digital BERT.', 'start': 360.827, 'duration': 5.164}, {'end': 366.912, 'text': 'It may be a GPT-2.', 'start': 366.271, 'duration': 0.641}, {'end': 374.438, 'text': 'So different types of pre-trained models will be able to call, and that pre-trained model has to be called with this pipeline library.', 'start': 367.572, 'duration': 6.866}, {'end': 381.865, 'text': 'Okay, so here you can see, once I import this, I just have to write pipeline, and I just have to give my one example of sentiment analysis.', 'start': 374.799, 'duration': 7.066}, {'end': 385.008, 'text': 'So this is basically a use case, sentiment analysis.', 'start': 381.885, 'duration': 3.123}], 'summary': 'Pipeline library enables easy use of pre-trained models for sentiment analysis.', 'duration': 28.485, 'max_score': 356.523, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/DkzbCJtFvqM/pics/DkzbCJtFvqM356523.jpg'}, {'end': 419.824, 'src': 'embed', 'start': 393.429, 'weight': 8, 'content': [{'end': 397.811, 'text': "I'll just talk about it and probably this will take some time for this particular model to get uploaded.", 'start': 393.429, 'duration': 4.382}, {'end': 404.715, 'text': "So guys, once you execute this here, you'll be seeing that that pre-trained model will get downloaded, and it may probably take some time.", 'start': 397.831, 'duration': 6.884}, {'end': 406.956, 'text': 'It depends on your internet speed, right?', 'start': 405.135, 'duration': 1.821}, {'end': 412.359, 'text': 'So, right now, this particular model will be focusing on doing sentiment analysis.', 'start': 407.356, 'duration': 5.003}, {'end': 414.2, 'text': "okay?. Now let's see one example.", 'start': 412.359, 'duration': 1.841}, {'end': 419.824, 'text': 'So here we have given like we are happy to show you the transformer library and we are just giving this classifier.', 'start': 414.26, 'duration': 5.564}], 'summary': 'A pre-trained model for sentiment analysis will be uploaded, may take time based on internet speed.', 'duration': 26.395, 'max_score': 393.429, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/DkzbCJtFvqM/pics/DkzbCJtFvqM393429.jpg'}, {'end': 454.828, 'src': 'embed', 'start': 427.771, 'weight': 5, 'content': [{'end': 433.195, 'text': "Which classifier or which problem model is getting called? Which BERT model is getting called? That I'm also going to discuss.", 'start': 427.771, 'duration': 5.424}, {'end': 436.016, 'text': "Now, you can see over here, I've given a sentence.", 'start': 433.715, 'duration': 2.301}, {'end': 439.298, 'text': 'We are happy to show you the Hugging Face transformer library.', 'start': 436.036, 'duration': 3.262}, {'end': 441.22, 'text': 'So this symbol is basically of the Hugging Face.', 'start': 439.359, 'duration': 1.861}, {'end': 446.183, 'text': "Now, if I go and execute it, here you'll be able to see that I'm getting a positive score.", 'start': 441.7, 'duration': 4.483}, {'end': 447.944, 'text': 'This also handles well.', 'start': 446.783, 'duration': 1.161}, {'end': 450.585, 'text': "The reason why I'm saying you that, let me just write over here.", 'start': 448.324, 'duration': 2.261}, {'end': 454.828, 'text': "I'll just say, I don't like pizza.", 'start': 451.806, 'duration': 3.022}], 'summary': 'Discussion of bert model and hugging face transformer library.', 'duration': 27.057, 'max_score': 427.771, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/DkzbCJtFvqM/pics/DkzbCJtFvqM427771.jpg'}], 'start': 183.313, 'title': 'Transformer learning for nlu and translation', 'summary': 'Covers setting up google colab pro for transformer learning, installing the transformers library, and showcasing the benefits. it also delves into the usage of pre-trained models for nlu tasks, focusing on bert architecture and its applications such as sentiment analysis, text generation, and translation. additionally, the chapter explains the usage of the pipeline to call pre-trained models like bert and gpt-2 for sentiment analysis, with a demonstration of executing sentiment analysis using the hugging face transformer library.', 'chapters': [{'end': 259.831, 'start': 183.313, 'title': 'Introduction to transformers', 'summary': 'Introduces the process of setting up google colab pro for transformer learning and emphasizes on the installation of the transformers library, highlighting the benefits and functionalities of transformers.', 'duration': 76.518, 'highlights': ['The chapter emphasizes on the installation of the transformers library, showcasing the process of setting up Google Colab Pro, and highlights the benefits and functionalities of transformers.', 'The speaker mentions the intention to learn and share knowledge about transformers, indicating the learning process and potential future video uploads.', 'The chapter discusses the importance of changing the runtime to GPU and having high RAM for Google Colab Pro, highlighting the technical requirements for efficient transformer learning.', 'The speaker outlines the plan to explore the capabilities of transformers and their applications in solving various tasks, indicating the focus on understanding the power of transformers.']}, {'end': 335.151, 'start': 260.432, 'title': 'Pre-trained models for nlu and translation', 'summary': 'Discusses the usage of pre-trained models for natural language understanding (nlu) tasks such as sentiment analysis, text generation, translation, and various other applications, with a focus on bert architecture and use cases including sentiment analysis, text generation, name entity recognition, question answering, summarization, and translation.', 'duration': 74.719, 'highlights': ['The chapter explores the usage of pre-trained models for NLU tasks such as sentiment analysis, text generation, and translation, highlighting the availability of models like BERT, GPT-2, Robota, XLM, Digital, BERT, XLNet, and their implementation.', 'It emphasizes the use cases of pre-trained models, including sentiment analysis, text generation, name entity recognition, question answering, summarization, and translation, showcasing the simplicity of implementation.']}, {'end': 446.183, 'start': 335.731, 'title': 'Using pipeline for pre-trained model', 'summary': 'Explains the usage of the pipeline to call pre-trained models like bert, gpt-2, and digital bert for sentiment analysis, with a demonstration of executing sentiment analysis using the hugging face transformer library.', 'duration': 110.452, 'highlights': ['The pipeline is used to call pre-trained models like BERT, GPT-2, and digital BERT for sentiment analysis.', 'Demonstration of executing sentiment analysis using the Hugging Face transformer library.', 'Explanation of the process where the pre-trained model gets downloaded and may take some time based on the internet speed.']}], 'duration': 262.87, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/DkzbCJtFvqM/pics/DkzbCJtFvqM183313.jpg', 'highlights': ['The chapter emphasizes on the installation of the transformers library, showcasing the process of setting up Google Colab Pro, and highlights the benefits and functionalities of transformers.', 'The chapter explores the usage of pre-trained models for NLU tasks such as sentiment analysis, text generation, and translation, highlighting the availability of models like BERT, GPT-2, Robota, XLM, Digital, BERT, XLNet, and their implementation.', 'The speaker outlines the plan to explore the capabilities of transformers and their applications in solving various tasks, indicating the focus on understanding the power of transformers.', 'It emphasizes the use cases of pre-trained models, including sentiment analysis, text generation, name entity recognition, question answering, summarization, and translation, showcasing the simplicity of implementation.', 'The pipeline is used to call pre-trained models like BERT, GPT-2, and digital BERT for sentiment analysis.', 'Demonstration of executing sentiment analysis using the Hugging Face transformer library.', 'The chapter discusses the importance of changing the runtime to GPU and having high RAM for Google Colab Pro, highlighting the technical requirements for efficient transformer learning.', 'The speaker mentions the intention to learn and share knowledge about transformers, indicating the learning process and potential future video uploads.', 'Explanation of the process where the pre-trained model gets downloaded and may take some time based on the internet speed.']}, {'end': 645.998, 'segs': [{'end': 579.737, 'src': 'embed', 'start': 551.381, 'weight': 1, 'content': [{'end': 557.443, 'text': "When I'm calling this classifier, what exactly is happening, why the sentiment analysis is able to give this particular output.", 'start': 551.381, 'duration': 6.062}, {'end': 558.504, 'text': "we'll try to understand.", 'start': 557.443, 'duration': 1.061}, {'end': 559.784, 'text': 'okay?, Now see this guys.', 'start': 558.504, 'duration': 1.28}, {'end': 561.485, 'text': 'The sentences are very much important.', 'start': 559.804, 'duration': 1.681}, {'end': 565.127, 'text': 'It is very much necessary that you read it line by line.', 'start': 562.065, 'duration': 3.062}, {'end': 571.151, 'text': 'if you really want to understand, probably, guys, this documentation that are given with respect to this transformer.', 'start': 566.127, 'duration': 5.024}, {'end': 579.737, 'text': 'it is pretty much amazing and, trust me, i will cover each and everything, each and every points over here with practical implementation, with, uh uh,', 'start': 571.151, 'duration': 8.586}], 'summary': 'Understanding sentiment analysis and transformer documentation for practical implementation.', 'duration': 28.356, 'max_score': 551.381, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/DkzbCJtFvqM/pics/DkzbCJtFvqM551381.jpg'}, {'end': 631.103, 'src': 'embed', 'start': 590.584, 'weight': 0, 'content': [{'end': 594.087, 'text': 'it needs to be positive or negative, but its score is fairly neutral.', 'start': 590.584, 'duration': 3.503}, {'end': 599.749, 'text': 'okay, now, this is the observation that we have made from this particular sentence right now, by default,', 'start': 594.087, 'duration': 5.662}, {'end': 602.31, 'text': 'no model downloaded for this particular pipeline.', 'start': 599.749, 'duration': 2.561}, {'end': 606.751, 'text': 'now, this sentiment analysis by default, which we have actually downloaded.', 'start': 602.31, 'duration': 4.441}, {'end': 609.832, 'text': 'we have downloaded something called as distal, but uncased.', 'start': 606.751, 'duration': 3.081}, {'end': 612.453, 'text': 'fine cues, sst to english.', 'start': 609.832, 'duration': 2.621}, {'end': 614.654, 'text': 'right now, why, what is all these things?', 'start': 612.453, 'duration': 2.201}, {'end': 619.276, 'text': 'guys, see this, This distilbert base is basically our model.', 'start': 614.654, 'duration': 4.622}, {'end': 623.478, 'text': 'Uncached, basically says that our data set, which is basically trained right?', 'start': 619.936, 'duration': 3.542}, {'end': 626.14, 'text': 'It is all trained on small letters right?', 'start': 623.538, 'duration': 2.602}, {'end': 627.981, 'text': 'All the words are in small letters right?', 'start': 626.2, 'duration': 1.781}, {'end': 631.103, 'text': 'And SST2 English is basically my data set.', 'start': 628.401, 'duration': 2.702}], 'summary': 'Sentiment analysis model is downloaded as distilbert uncased, trained on sst2 english dataset with small letters.', 'duration': 40.519, 'max_score': 590.584, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/DkzbCJtFvqM/pics/DkzbCJtFvqM590584.jpg'}], 'start': 446.783, 'title': 'Sentiment analysis', 'summary': 'Explores sentiment analysis using a specific library, demonstrating the classification of texts and their associated scores, with examples showing positive scores reaching 0.99 and negative scores at 0.53, as well as the ability to handle multiple texts and classify them based on different categories. it also covers the process of sentiment analysis using a specific model and dataset, emphasizing the importance of understanding the documentation and practical implementation.', 'chapters': [{'end': 550.64, 'start': 446.783, 'title': 'Sentiment analysis: transformer library', 'summary': 'Explores sentiment analysis using a specific library, demonstrating the classification of texts and their associated scores, with examples showing positive scores reaching 0.99 and negative scores at 0.53, as well as the ability to handle multiple texts and classify them based on different categories.', 'duration': 103.857, 'highlights': ['The chapter discusses sentiment analysis using a specific library and demonstrates the classification of texts with associated scores, including positive scores reaching 0.99 and negative scores at 0.53.', 'It highlights the ability to handle multiple texts and classify them based on different categories.', 'The transcript emphasizes the importance of understanding and utilizing the specific library for sentiment analysis.']}, {'end': 645.998, 'start': 551.381, 'title': 'Understanding sentiment analysis process', 'summary': 'Explores the process of sentiment analysis using a specific model and dataset, covering the classification of sentences and the default sentiment analysis output, emphasizing the importance of understanding the documentation and practical implementation.', 'duration': 94.617, 'highlights': ['The documentation provided for the transformer model is emphasized for understanding and practical implementation, ensuring a comprehensive grasp of the process.', 'Observing the default sentiment analysis output, it is noted that the second sentence is classified as negative, but its score is fairly neutral, indicating the need for a clear understanding of the classification process.', "Explanation of the model 'DistilBERT base uncased' and the dataset 'SST2 English' used for sentiment analysis, highlighting the importance of understanding different types of models and their associated datasets."]}], 'duration': 199.215, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/DkzbCJtFvqM/pics/DkzbCJtFvqM446783.jpg', 'highlights': ['The chapter discusses sentiment analysis using a specific library and demonstrates the classification of texts with associated scores, including positive scores reaching 0.99 and negative scores at 0.53.', 'The ability to handle multiple texts and classify them based on different categories is highlighted.', 'The documentation provided for the transformer model is emphasized for understanding and practical implementation, ensuring a comprehensive grasp of the process.', 'Observing the default sentiment analysis output, it is noted that the second sentence is classified as negative, but its score is fairly neutral, indicating the need for a clear understanding of the classification process.', "Explanation of the model 'DistilBERT base uncased' and the dataset 'SST2 English' used for sentiment analysis, highlighting the importance of understanding different types of models and their associated datasets.", 'The transcript emphasizes the importance of understanding and utilizing the specific library for sentiment analysis.']}, {'end': 808.498, 'segs': [{'end': 671.726, 'src': 'embed', 'start': 646.478, 'weight': 0, 'content': [{'end': 652.184, 'text': 'This model is a fine-tuned checkpoint of distilled bird based uncaged fine-tuned on SST2.', 'start': 646.478, 'duration': 5.706}, {'end': 654.146, 'text': 'That basically means this is basically my data set.', 'start': 652.324, 'duration': 1.822}, {'end': 659.332, 'text': 'The model reaches an accuracy of 91.3 on the Devset for comparison.', 'start': 654.566, 'duration': 4.766}, {'end': 661.875, 'text': 'BERT-based uncached version reaches 92.7.', 'start': 659.812, 'duration': 2.063}, {'end': 666.18, 'text': 'So here it is saying that BERT probably performs better than Distilled BERT.', 'start': 661.875, 'duration': 4.305}, {'end': 671.726, 'text': 'And what are the parameters that are taken in this? It is somewhere around learning rate, batch size, warm-up and all.', 'start': 666.58, 'duration': 5.146}], 'summary': 'Distilled bert achieves 91.3% accuracy on devset, while bert reaches 92.7%.', 'duration': 25.248, 'max_score': 646.478, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/DkzbCJtFvqM/pics/DkzbCJtFvqM646478.jpg'}, {'end': 790.973, 'src': 'embed', 'start': 750.381, 'weight': 2, 'content': [{'end': 755.604, 'text': 'Suppose I want to work with a model that will be able to do text classification based on French data.', 'start': 750.381, 'duration': 5.223}, {'end': 759.506, 'text': 'Then we can search that specific model from this model hub.', 'start': 756.224, 'duration': 3.282}, {'end': 766.21, 'text': 'Now model hub is basically the hub of the entire Hugging Face models, right? For a specific class.', 'start': 759.986, 'duration': 6.224}, {'end': 768.771, 'text': 'Now suppose I want to do text classification.', 'start': 766.25, 'duration': 2.521}, {'end': 775.246, 'text': 'okay, now, in this test classification we have applied distilled burst, uncased, fine-tuned ssd2.', 'start': 769.884, 'duration': 5.362}, {'end': 777.847, 'text': 'right now, suppose i want to do it for multiple languages.', 'start': 775.246, 'duration': 2.601}, {'end': 781.869, 'text': 'then i can go and select this nlp town bird based, multilingual uncased.', 'start': 777.847, 'duration': 4.022}, {'end': 786.711, 'text': 'if i go and select this here, it will tell you that what all languages it actually supports.', 'start': 781.869, 'duration': 4.842}, {'end': 790.973, 'text': 'it suppose english, dutch, german, french, spanish and italian italian.', 'start': 786.711, 'duration': 4.262}], 'summary': 'Using hugging face model hub for text classification on multiple languages including french, with support for english, dutch, german, spanish, and italian.', 'duration': 40.592, 'max_score': 750.381, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/DkzbCJtFvqM/pics/DkzbCJtFvqM750381.jpg'}], 'start': 646.478, 'title': 'Using distilbert for multilingual sentiment analysis', 'summary': 'Discusses the application of distilbert for sentiment analysis on multilingual data, achieving a 91.3% accuracy on the sst2 devset and accessing diverse models for various languages and tasks in the hugging face model hub.', 'chapters': [{'end': 808.498, 'start': 646.478, 'title': 'Distilbert for multilingual sentiment analysis', 'summary': 'Discusses the usage of distilbert for sentiment analysis on a multilingual dataset, achieving an accuracy of 91.3% on the sst2 devset and the availability of other models for different languages and tasks within the hugging face model hub.', 'duration': 162.02, 'highlights': ['The model reaches an accuracy of 91.3 on the Devset for comparison.', 'BERT-based uncached version reaches 92.7.', 'Hugging Face model hub offers models for different languages and tasks.', 'nlp town bird based, multilingual uncased supports multiple languages including English, Dutch, German, French, Spanish, and Italian.']}], 'duration': 162.02, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/DkzbCJtFvqM/pics/DkzbCJtFvqM646478.jpg', 'highlights': ['BERT-based uncached version reaches 92.7.', 'The model reaches an accuracy of 91.3 on the Devset for comparison.', 'Hugging Face model hub offers models for different languages and tasks.', 'nlp town bird based, multilingual uncased supports multiple languages including English, Dutch, German, French, Spanish, and Italian.']}, {'end': 1259.166, 'segs': [{'end': 952.369, 'src': 'embed', 'start': 908.093, 'weight': 0, 'content': [{'end': 912.235, 'text': 'Here, this is a very very positive sentiment analysis, so it is saying five star.', 'start': 908.093, 'duration': 4.142}, {'end': 914.136, 'text': 'Then we copy and paste this.', 'start': 912.975, 'duration': 1.161}, {'end': 919.46, 'text': 'It is also going to give me 1 star of 0.453, 2 star of this, 3 star of this.', 'start': 914.877, 'duration': 4.583}, {'end': 922.143, 'text': 'So these are all the information that is basically given.', 'start': 919.861, 'duration': 2.282}, {'end': 931.17, 'text': 'So what are the accuracy with respect to English language? You can see 65, 67% exact of by 1, 95%.', 'start': 923.123, 'duration': 8.047}, {'end': 934.693, 'text': 'So these are some of the metrics they have performed and they have given the accuracy.', 'start': 931.17, 'duration': 3.523}, {'end': 937.484, 'text': "okay, now let's try with something like.", 'start': 935.123, 'duration': 2.361}, {'end': 939.465, 'text': 'i hope this also supports.', 'start': 937.484, 'duration': 1.981}, {'end': 942.225, 'text': 'this is also good because right hand side you can see the examples.', 'start': 939.465, 'duration': 2.76}, {'end': 945.487, 'text': 'also, guys, okay, and probably you can test which all things are there.', 'start': 942.225, 'duration': 3.262}, {'end': 947.987, 'text': "let's convert this into spanish.", 'start': 945.487, 'duration': 2.5}, {'end': 952.369, 'text': 'now i think spanish is also supported.', 'start': 947.987, 'duration': 4.382}], 'summary': 'Positive sentiment analysis: 5-star rating, 65-67% accuracy in english language.', 'duration': 44.276, 'max_score': 908.093, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/DkzbCJtFvqM/pics/DkzbCJtFvqM908093.jpg'}, {'end': 1078.988, 'src': 'heatmap', 'start': 1011.758, 'weight': 4, 'content': [{'end': 1013.82, 'text': "So almost you're getting the same output, okay?", 'start': 1011.758, 'duration': 2.062}, {'end': 1019.563, 'text': 'The classifier now deal with text in English, French, but also Dutch, German, Italian and Spanish.', 'start': 1014.46, 'duration': 5.103}, {'end': 1024.046, 'text': 'You can also replace that name by a local folder where you have save train model.', 'start': 1019.843, 'duration': 4.203}, {'end': 1028.729, 'text': 'okay?. You can also download the save train model, probably in a local folder, and you can also do it okay?', 'start': 1024.046, 'duration': 4.683}, {'end': 1032.231, 'text': 'Now, this is very much important guys.', 'start': 1029.388, 'duration': 2.843}, {'end': 1038.836, 'text': 'Now, we really need to understand what exactly is actually happening, okay? And right now, we have called like this.', 'start': 1032.371, 'duration': 6.465}, {'end': 1044.579, 'text': 'Now, what if I want to call it to a local folder and probably do it, okay? And for this, the steps are something like this.', 'start': 1038.896, 'duration': 5.683}, {'end': 1046.861, 'text': "So here, I'm going to use TensorFlow.", 'start': 1045.079, 'duration': 1.782}, {'end': 1054.026, 'text': 'So I have to use from transformers, import autotokenizer and tf.orderModel for sequence classification.', 'start': 1047.281, 'duration': 6.745}, {'end': 1056.428, 'text': 'Now, very, very important information, guys.', 'start': 1054.566, 'duration': 1.862}, {'end': 1064.717, 'text': "If you're working with these transformers of this hugging phase, always remember whenever we call any kind of model,", 'start': 1057.669, 'duration': 7.048}, {'end': 1068.841, 'text': 'probably we want to call it in our local and probably we need to fine tune train it.', 'start': 1064.717, 'duration': 4.124}, {'end': 1071.644, 'text': "we have to usually go with this process what I'm actually going to show you.", 'start': 1068.841, 'duration': 2.803}, {'end': 1073.986, 'text': 'Okay But remember one thing.', 'start': 1072.225, 'duration': 1.761}, {'end': 1078.988, 'text': 'Whenever we need to call this particular model, we also need to call the tokenizer.', 'start': 1074.586, 'duration': 4.402}], 'summary': 'The classifier deals with text in multiple languages and requires fine-tuning for local implementation.', 'duration': 67.23, 'max_score': 1011.758, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/DkzbCJtFvqM/pics/DkzbCJtFvqM1011758.jpg'}], 'start': 809.278, 'title': 'Nlp model training & tokenization', 'summary': 'Covers training and tokenization of nlp models using transformers from hugging face, emphasizing the importance of tokenizer and the process of fine-tuning and training with tensorflow, achieving an accuracy level of four for the sentiment analysis model.', 'chapters': [{'end': 1011.458, 'start': 809.278, 'title': 'Text classification with bert model', 'summary': 'Discusses how to specify and use the bert model for text classification, performing sentiment analysis, and evaluating accuracy metrics, achieving 95% accuracy for english language and supporting multiple languages like french, italian, and spanish.', 'duration': 202.18, 'highlights': ['The BERT model is used for text classification, achieving 95% accuracy for English language.', 'Performing sentiment analysis resulted in accurate star ratings, with a very positive sentiment being classified as five stars and achieving an accuracy of 67%.', 'Support for multiple languages such as French, Italian, and Spanish is demonstrated, with the model providing accurate sentiment analysis and achieving a maximum accuracy of 65% for Spanish language.']}, {'end': 1259.166, 'start': 1011.758, 'title': 'Nlp model training & tokenization', 'summary': 'Covers training and tokenization of nlp models using transformers from hugging face, emphasizing the importance of tokenizer and the process of fine-tuning and training with tensorflow, and provides insights on language support and model usage, with the sentiment analysis model achieving an accuracy level of four.', 'duration': 247.408, 'highlights': ['The chapter discusses the process of fine-tuning and training NLP models using transformers from Hugging Face, with emphasis on the importance of tokenization and the need to call the tokenizer when working with NLP models, particularly in TensorFlow.', 'It provides insights on the language support of the classifier, which includes English, French, Dutch, German, Italian, and Spanish.', 'The process of downloading and using the pre-trained model and tokenizer from a local folder is explained, along with the significance of tokenizing text data and its role in converting text into numerical data for classification.', 'The sentiment analysis model achieves an accuracy level of four, indicating a high level of accuracy in classifying text data.']}], 'duration': 449.888, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/DkzbCJtFvqM/pics/DkzbCJtFvqM809278.jpg', 'highlights': ['The BERT model achieves 95% accuracy for English language text classification.', 'The sentiment analysis model achieves an accuracy level of four, indicating high accuracy.', 'Very positive sentiment is classified as five stars with 67% accuracy.', 'Support for multiple languages demonstrated, achieving a maximum accuracy of 65% for Spanish.', 'Insights on the language support of the classifier, including English, French, Dutch, German, Italian, and Spanish.']}, {'end': 1469.099, 'segs': [{'end': 1371.53, 'src': 'embed', 'start': 1323.907, 'weight': 1, 'content': [{'end': 1327.17, 'text': "Now this is downloaded, okay? Now let's see one step.", 'start': 1323.907, 'duration': 3.263}, {'end': 1332.554, 'text': 'Suppose, if I take this tokenizer, this tokenizer is nothing, but this is initialized from auto tokenizer,', 'start': 1327.67, 'duration': 4.884}, {'end': 1335.957, 'text': 'from that pre-trained model which is given as a model name over here.', 'start': 1332.554, 'duration': 3.403}, {'end': 1337.518, 'text': 'I have my tokenizer variable.', 'start': 1336.317, 'duration': 1.201}, {'end': 1340.621, 'text': "I'm just writing, we are happy to show you the transformer library.", 'start': 1337.918, 'duration': 2.703}, {'end': 1346.486, 'text': "now let's see how this entire word is converted into a tokens.", 'start': 1341.241, 'duration': 5.245}, {'end': 1351.792, 'text': 'okay, so i will make a code and now, oh, here it is print of inputs.', 'start': 1346.486, 'duration': 5.306}, {'end': 1359.92, 'text': 'if i input this here, you can see that every input id, every word, is basically given by a numerical number.', 'start': 1351.792, 'duration': 8.128}, {'end': 1364.928, 'text': 'Okay, this numerical number is basically treated as IDs of the tokens.', 'start': 1360.566, 'duration': 4.362}, {'end': 1368.789, 'text': 'Okay, these are basically the IDs of the tokens, like how we do it in Word2Vec.', 'start': 1365.108, 'duration': 3.681}, {'end': 1371.53, 'text': 'We provide some numerical values, probably some indexes.', 'start': 1369.069, 'duration': 2.461}], 'summary': 'Demonstration of tokenization process using transformer library with numerical ids assigned to tokens.', 'duration': 47.623, 'max_score': 1323.907, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/DkzbCJtFvqM/pics/DkzbCJtFvqM1323907.jpg'}, {'end': 1469.099, 'src': 'embed', 'start': 1417.804, 'weight': 0, 'content': [{'end': 1419.886, 'text': 'See, everything is shown over here.', 'start': 1417.804, 'duration': 2.082}, {'end': 1422.688, 'text': 'Pretty much easy, pretty much simple.', 'start': 1421.347, 'duration': 1.341}, {'end': 1424.17, 'text': 'Here we have given two sentences.', 'start': 1422.728, 'duration': 1.442}, {'end': 1425.871, 'text': 'This is one sentence, this is the other sentence.', 'start': 1424.21, 'duration': 1.661}, {'end': 1434.159, 'text': 'Now, when we are given two sentences, our tokenization process should make this particular sentence equally by using this padding and max length.', 'start': 1426.412, 'duration': 7.747}, {'end': 1436.881, 'text': 'This I have entirely covered in my NLP playlist.', 'start': 1434.579, 'duration': 2.302}, {'end': 1437.922, 'text': 'You really need to understand.', 'start': 1436.901, 'duration': 1.021}, {'end': 1440.625, 'text': 'We are just reusing it with the state-of-art algorithm.', 'start': 1437.962, 'duration': 2.663}, {'end': 1443.026, 'text': 'right. so tokenizer process,', 'start': 1440.805, 'duration': 2.221}, {'end': 1450.21, 'text': 'it actually helps you to create some good indexes and the indexes will be similar to all the sentences that we are going ahead with right.', 'start': 1443.026, 'duration': 7.184}, {'end': 1453.611, 'text': "so probably we'll try to discuss more about this as we go ahead.", 'start': 1450.21, 'duration': 3.401}, {'end': 1462.996, 'text': 'but i hope you like the introduction of the transformers and every day one video will be uploaded and along with that the material will also be shared to you.', 'start': 1453.611, 'duration': 9.385}, {'end': 1464.777, 'text': 'okay, so i hope you like this particular video.', 'start': 1462.996, 'duration': 1.781}, {'end': 1465.837, 'text': 'please do subscribe the channel.', 'start': 1464.777, 'duration': 1.06}, {'end': 1467.358, 'text': "if you're not subscribed, i'll see you in the next video.", 'start': 1465.837, 'duration': 1.521}, {'end': 1467.978, 'text': 'have a great day.', 'start': 1467.358, 'duration': 0.62}, {'end': 1469.099, 'text': 'thank you, one dot bye.', 'start': 1467.978, 'duration': 1.121}], 'summary': 'Tokenization process explained with use of padding and max length in nlp playlist. state-of-art algorithm for creating indexes. regular video uploads and shared materials.', 'duration': 51.295, 'max_score': 1417.804, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/DkzbCJtFvqM/pics/DkzbCJtFvqM1417804.jpg'}], 'start': 1260.506, 'title': 'Tokenization process', 'summary': 'Explains the tokenization process in huggingface and nlp, showcasing the conversion of words to tokens, use of padding, maximum length, and index creation, with a focus on a maximum length of 512 and educational resources available.', 'chapters': [{'end': 1371.53, 'start': 1260.506, 'title': 'Tokenization process in huggingface', 'summary': 'Explains the tokenization process in huggingface, using the example of the distil-bert-uncached-find-sht2-english model, and demonstrates how words are converted into tokens, represented by numerical ids.', 'duration': 111.024, 'highlights': ['The tokenizer is initialized from auto tokenizer, from that pre-trained model which is given as a model name over here.', 'Every word is converted into tokens, represented by numerical IDs.', 'The specific model is trained using the tokenization process, which leads to the generation of numerical IDs for the tokens.']}, {'end': 1469.099, 'start': 1371.97, 'title': 'Understanding tokenizer process in nlp', 'summary': 'Explains the process of tokenization in natural language processing, including the use of padding and maximum length to standardize sentences, with a focus on a maximum length of 512, and the creation of indexes to aid in nlp tasks, also highlighting the availability of related educational content.', 'duration': 97.129, 'highlights': ['The process of tokenization involves using padding and max length, with a focus on a maximum length of 512, to standardize sentences for NLP tasks, ensuring equal sentence lengths for effective processing.', 'The tokenizer process aids in creating good indexes for sentences, facilitating NLP tasks, and is covered in the NLP playlist, with regular educational content updates.', 'The introduction of transformers is mentioned, with a commitment to daily video uploads and supplementary educational materials for subscribers.']}], 'duration': 208.593, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/DkzbCJtFvqM/pics/DkzbCJtFvqM1260506.jpg', 'highlights': ['The process of tokenization involves using padding and max length, with a focus on a maximum length of 512, to standardize sentences for NLP tasks, ensuring equal sentence lengths for effective processing.', 'The tokenizer is initialized from auto tokenizer, from that pre-trained model which is given as a model name over here.', 'The specific model is trained using the tokenization process, which leads to the generation of numerical IDs for the tokens.', 'The tokenizer process aids in creating good indexes for sentences, facilitating NLP tasks, and is covered in the NLP playlist, with regular educational content updates.', 'The introduction of transformers is mentioned, with a commitment to daily video uploads and supplementary educational materials for subscribers.', 'Every word is converted into tokens, represented by numerical IDs.']}], 'highlights': ['The BERT model achieves 95% accuracy for English language text classification.', 'BERT-based uncached version reaches 92.7.', 'The model reaches an accuracy of 91.3 on the Devset for comparison.', 'The sentiment analysis model achieves an accuracy level of four, indicating high accuracy.', 'Transformers library by HuggingFace provides state-of-the-art algorithms for NLP tasks with support for BERT, GPT-2, and Distilbert', 'The importance of the BERT model and its usage for various NLP tasks is emphasized', 'The chapter emphasizes on the installation of the transformers library, showcasing the process of setting up Google Colab Pro, and highlights the benefits and functionalities of transformers.', 'The pipeline is used to call pre-trained models like BERT, GPT-2, and digital BERT for sentiment analysis.', 'The process of tokenization involves using padding and max length, with a focus on a maximum length of 512, to standardize sentences for NLP tasks, ensuring equal sentence lengths for effective processing.', 'The tokenizer is initialized from auto tokenizer, from that pre-trained model which is given as a model name over here.']}