https://www.youtube.com/watch?v=90mGPxR2GgY
Full explanation of the BERT model, including a comparison with other language models like LLaMA and GPT. I cover topics like: training, inference, fine tuni

https://huggingface.co/blog/bert-101
BERT is a highly complex and advanced language model that helps people automate language understanding. Its ability to accomplish state-of-the-art performance is supported by training on massive amounts of data and leveraging Transformers architecture to revolutionize the field of NLP.

https://datascience.stackexchange.com/questions/123053/why-does-everyone-use-bert-in-research-instead-of-llama-or-gpt-or-palm-etc/
To use Llama for inference you need a lot of very powerful GPUs, let alone training it. Most research groups have modest computational resources. Appropriateness for downstream tasks: BERT is easily applied to text classification because it has the output at the [CLS] token position, which can be directly attached a classification head. Llama

https://lossoptimization.substack.com/p/bert-vs-gpt-choosing-the-right-model
Surprisingly, they found that BERT-based models produce summaries with higher factual consistency compared to GPT-based models. This suggests that BERT's bidirectional context understanding helps in generating more accurate and faithful summaries, even though GPT is typically stronger in language generation.

https://www.coursera.org/articles/bert-vs-gpt
Advantages of BERT. Though ChatGPT and BERT use the transformer architecture, they differ in how they specifically process and generate language. BERT uses bidirectional context representation, which processes text from right to left and left to right. This allows BERT an increased capability to generate language based on context.

https://www.towardsnlp.com/bert-vs-gpt-3-comparing-two-powerhouse-language-models/
Conclusion. In the battle of BERT vs. GPT-3, there is no clear winner. These language models cater to different NLP needs, with BERT excelling in understanding context and semantics, and GPT-3 dominating generative tasks. The choice between them depends on the specific application and requirements.

https://towardsdatascience.com/a-complete-guide-to-bert-with-code-9f87602e4a11
An overview of the BERT embedding process. Image taken from the BERT paper [1]. 2.5 — The Special Tokens. In the image above, you may have noted that the input sequence has been prepended with a [CLS] (classification) token. This token is added to encapsulate a summary of the semantic meaning of the entire input sequence, and helps BERT to perform classification tasks.

https://towardsdatascience.com/keeping-up-with-the-berts-5b7beb92766
Fine-tuning BERT on various downstream tasks. Source: The paper. In Sentence Pair Classification and Single Sentence Classification, the final state corresponding to [CLS] token is used as input for the additional layers that makes the prediction.In QA tasks, a start (S) and an end (E) vector are introduced during fine tuning.

https://medium.com/@samia.khalid/bert-explained-a-complete-guide-with-theory-and-tutorial-3ac9ebc8fa7c
Here is the link to this code on git.. 3. Training Model using Pre-trained BERT model. Some checkpoints before proceeding further: All the .tsv files should be in a folder called "data" in the

https://paperswithcode.com/method/bert
BERT, or Bidirectional Encoder Representations from Transformers, improves upon standard Transformers by removing the unidirectionality constraint by using a masked language model (MLM) pre-training objective. The masked language model randomly masks some of the tokens from the input, and the objective is to predict the original vocabulary id of the masked word based only on its context

https://www.makeuseof.com/gpt-vs-bert/
Training Data. BERT and GPT differ in the types of training data they use. BERT is trained using a masked language model, meaning certain words are masked, and the algorithm has to predict what the next word is likely to be. This helps train the model and makes it more contextually accurate. Like GPT, BERT is trained on a large-scale corpus of

https://medium.com/dataness-ai/exploring-bert-feature-extraction-fine-tuning-6d6ad7b829e7
Figure 5: Fine-tuning BERT for token classification. Question answering: takes as input two text sequences, where the first one is the question and the second one is the passage that the question

https://powerbrainai.com/bert-vs-gpt/
Explore the face-off between BERT and GPT, two impactful Transformers-based models in natural language processing. Learn how BERT's bidirectional features outpace in tasks like Named Entity Recognition and Question Answering, while GPT shines in text generation tasks. Discover their distinct specialties, language support, and effective handling of out-of-vocabulary words.

https://medium.com/@ayikfurkan1/mastering-text-classification-with-bert-a-comprehensive-guide-194ddb2aa2e5
BERT stands out due to its bidirectional nature, enabling it to consider the full context of a word by analyzing both its preceding and subsequent words in a sequence. This bidirectional

https://datascience.stackexchange.com/questions/66207/what-is-purpose-of-the-cls-token-and-why-is-its-encoding-output-important
In order to better understand the role of [CLS] let's recall that BERT model has been trained on 2 main tasks: Masked language modeling: some random words are masked with [MASK] token, the model learns to predict those words during training.

https://www.linkedin.com/posts/ujamil_bert-explained-training-inference-bert-activity-7123117347250307072-LIAs
I couldn't find an extensive comparison of these two worlds: fine tuning vs prompting. That's why I decided to make a new video in which I explore BERT, but also compare it with LLMs like LLaMA

https://stackoverflow.com/questions/62705268/why-bert-transformer-uses-cls-token-for-classification-instead-of-average-over
The use of the [CLS] token to represent the entire sentence comes from the original BERT paper, section 3:. The first token of every sequence is always a special classification token ([CLS]). The final hidden state corresponding to this token is used as the aggregate sequence representation for classification tasks.

https://www.linkedin.com/pulse/bert-vs-gpt-which-better-llm-model-ronak-verma-xeixc
The Verdict: It Depends on the Task. Choosing between BERT and GPT as the superior LLM model ultimately depends on the specific task at hand. If precision and deep contextual understanding are

https://medium.com/bright-ml/nlp-deep-learning-models-difference-between-bert-gpt-3-f273e67597d7
4) Training a) General tasks: Trained for general tasks.b) Fine-tuning for transfer learning: less option for fine-tuning compared to BERT.But require less training data for fine-tuning. GPT-3

https://www.reddit.com/r/deeplearning/comments/17gmtxr/bert_explained_training_masked_language_model/
BERT explained: Training (Masked Language Model, Next Sentence Prediction), Inference, Self-Attention, [CLS] token, Left and Right context, Comparative analysis BERT vs GPT/LLamA, Fine tuning, Text Classification, Question Answering. comments sorted by Best Top New Controversial Q&A Add a Comment. More posts you may like

https://www.reddit.com/r/learnmachinelearning/comments/17gmrxz/bert_explained_training_masked_language_model/
BERT explained: Training (Masked Language Model, Next Sentence Prediction), Inference, Self-Attention, [CLS] token, Left and Right context, Comparative analysis BERT vs GPT/LLamA, Fine tuning, Text Classification, Question Answering ... LLMs trained with a finite attention window can be extended to infinite sequence lengths without any fine-tuning.

https://www.oreilly.com/live-events/hands-on-transfer-learning-for-nlp-with-bert/0636920061282/
We will then move into examples of fine-tuning BERT on domain-specific corpora and using pre-trained models to perform NLP tasks out of the box. BERT is one of the most relevant NLP architectures today and it is closely related to other important NLP deep learning models like GPT-3.

https://medium.com/@philipp-gabriel/understanding-bert-fd7c461dbb78
The fine-tuning approach was becoming more popular immediately before the release of BERT and used by various teams such as OpenAI in GPT [4] about a year before BERT. With fine-tuning, the exact

https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-024-05847-x
LLMs, exemplified by architectures like BERT [], GPT [], LLaMA [], and LLaMA2 [] excel at understanding context within sentences and generating coherent text.They leverage attention mechanisms and vast training data to capture contextual information, making them versatile for text generation, translation, and sentiment analysis tasks.

https://www.nature.com/articles/s41586-024-07421-0
Hallucinations (confabulations) in large language model systems can be tackled by measuring uncertainty about the meanings of generated responses rather than the text itself to improve