Pradip Nichite
Pradip Nichite

@pradip_nichite

18 Tweets 19 reads Oct 22, 2022
NLP Roadmap 2022 with free resources.
This is what you need to build real-world NLP Projects and a Good Foundation. A Thread ๐Ÿงต๐Ÿ‘‡
๐ŸŽฏ Text Pre-Processing (Use #spacy):
๐Ÿ‘‰ Tokenization
๐Ÿ‘‰ Lemmatization
๐Ÿ‘‰ Removing Punctuations and Stopwords etc.
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๐ŸŽฏ Text Representation Techniques (Feature Engineering):
๐Ÿ‘‰ Bag of Words, Count Vector - #Sklearn
๐Ÿ‘‰ TFIDF - #Sklearn
๐Ÿ‘‰ Word2Vec - #Gensim
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๐Ÿ“Œ Task:
๐Ÿ‘‰ Build Text Classification model using algorithms like Logistic Regression, Random Forest, Xgboost, etc., and features from Count Vector, TFIDF, and Word2Vec.
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๐ŸŽฏ Learn Neural Networks and Deep Learning (Irrespective of whether you want to learn NLP or Computer Vision)
Try Hands-on with Pytorch or Tensorflow.
๐ŸŽฏ Information Extraction (Use Spacy):
๐Ÿ‘‰ POS tagging
๐Ÿ‘‰ Dependency parser
๐Ÿ‘‰ Named Entity Recognition
๐Ÿ“Œ Task:
Learn how to use a pre-trained model from #Spacy for #NER and.
How to build a custom NER model.
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๐ŸŽฏ Transfer Learning and Transformers Overview:
๐Ÿ‘‰ Read about Transfer Learning and Transformers Architecture.
๐Ÿ‘‰ Learn to use a pre-trained model from Hugging Face Transformers.
#transferlearning
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๐Ÿ‘‰ Learn How to fine-tune transformer models like BERT on Custom Dataset.
๐Ÿ‘‰ Learn How to push fine-tuned model to the hugging face model hub and load it into your deployment environment
@huggingface #transformers
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๐ŸŽฏ Deploy Machine Learning Model:
๐Ÿ‘‰ Integrate your NLP ML model into Streamlit and deploy it on the Streamlit cloud (or Heroku)
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๐Ÿ‘‰ Expose Model as Rest API using. Use #FastAPI or #Flask and deploy it on AWS Cloud.
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๐Ÿ‘‰ Generate Sentence Embedding using Sentence Transformers (Text Representation).
๐Ÿ‘‰ Use Sentence embedding for clustering documents, semantic search, etc.
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๐Ÿ‘‰ Build a classification model using Sentence Transformers' features and fit it to algorithms like Random Forest and Xgboost.
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๐ŸŽฏ Build NLP products using Language models like GPT-3
๐Ÿ‘‰ Learn how to use #gpt3 playground to check feasibility (GPT-3 prompt design).
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๐Ÿ‘‰ Integrate GPT-3 Prompt into code.
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๐Ÿ‘‰ Fine-tune GPT-3.
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๐ŸŽฏ Solve popular NLP Tasks:
1. Text Classification.
2. Sentiment Analysis (Aspect Based Sentiment Analysis).
3. Document Clustering.
4. Topic Modeling.
5. Named Entity Recognition.
๐Ÿ‘ Additionally:
6. Semantic Search.
7. Question Answering.
8. Conversational AI (Chatbot).

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