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Transformers vs rasa NLU: What are the differences?

Introduction

In this article, we will compare and discuss the key differences between Transformers and Rasa NLU in Markdown format for website integration.

  1. Pipeline Structure: Transformers use a pipeline-based structure where multiple tasks like text classification, named entity recognition, and question answering can be performed. On the other hand, Rasa NLU follows an intent classification and entity recognition structure, focusing more on natural language understanding.

  2. Model Architecture: Transformers primarily use the self-attention mechanism, allowing the model to weigh the importance of different words in a sentence. Rasa NLU, on the other hand, uses a combination of machine learning algorithms like Conditional Random Fields (CRF) to perform the intent and entity recognition tasks.

  3. Pre-training vs Fine-tuning: Transformers are pre-trained on large-scale corpora and then fine-tuned for specific tasks. This allows the model to generalize well and perform efficiently on various NLP tasks. In contrast, Rasa NLU does not use pre-training; it learns directly from labeled training data and requires specific training for each task.

  4. Availability of Pretrained Models: Transformers, with their pre-training and fine-tuning mechanism, provide a wide range of pre-trained models in multiple languages, making them readily available for different NLP tasks. Rasa NLU, being more focused on a specific intent recognition and entity extraction, does not offer a wide range of pre-trained models.

  5. Ease of Use: Transformers provide a more user-friendly interface and a high-level API to perform various NLP tasks. It simplifies the process of developing NLP models and requires relatively less coding effort. On the other hand, Rasa NLU requires more manual configuration and coding to set up and train the model.

  6. Integration with Dialogue Systems: Transformers are primarily used for specific NLP tasks and may require additional modules or frameworks for building dialogue systems. Rasa NLU, being a part of the Rasa ecosystem, seamlessly integrates with Rasa Core, allowing the development of end-to-end conversational agents.

In summary, Transformers and Rasa NLU differ in their pipeline structure, model architecture, pre-training, availability of pre-trained models, ease of use, and integration with dialogue systems.

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Pros of rasa NLU
Pros of Transformers
  • 9
    Open Source
  • 6
    Docker Image
  • 6
    Self Hosted
  • 3
    Comes with rasa_core
  • 1
    Enterprise Ready
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    Cons of rasa NLU
    Cons of Transformers
    • 4
      No interface provided
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      What is rasa NLU?

      rasa NLU (Natural Language Understanding) is a tool for intent classification and entity extraction. You can think of rasa NLU as a set of high level APIs for building your own language parser using existing NLP and ML libraries.

      What is Transformers?

      It provides general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet…) for Natural Language Understanding (NLU) and Natural Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between TensorFlow 2.0 and PyTorch.

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      What are some alternatives to rasa NLU and Transformers?
      Dialogflow
      Give users new ways to interact with your product by building engaging voice and text-based conversational apps.
      SpaCy
      It is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products. It comes with pre-trained statistical models and word vectors, and currently supports tokenization for 49+ languages.
      NLTK
      It is a suite of libraries and programs for symbolic and statistical natural language processing for English written in the Python programming language.
      TensorFlow
      TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
      Postman
      It is the only complete API development environment, used by nearly five million developers and more than 100,000 companies worldwide.
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