https://github.com/avinashbarnwal/nlp

Learning NLP
https://github.com/avinashbarnwal/nlp

Last synced: 24 days ago
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Learning NLP

• Host: GitHub
• URL: https://github.com/avinashbarnwal/nlp
• Owner: avinashbarnwal
• Created: 2019-10-22T21:46:08.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2024-09-07T14:10:31.000Z (2 months ago)
• Last Synced: 2024-09-07T15:39:27.944Z (2 months ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 4.01 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
## NLP

### Learning NLP Systematically

#### Link

- https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/discussion/91432#latest-636275

### Word2Vec

#### Link

- https://towardsdatascience.com/an-implementation-guide-to-word2vec-using-numpy-and-google-sheets-13445eebd281

#### Classification using BERT

- https://www.kaggle.com/anushakarthik1991/nlp-with-disaster-tweets-eda-cleaning-and-bert

#### Multilabel

- https://towardsdatascience.com/journey-to-the-center-of-multi-label-classification-384c40229bff

#### BERT

- Question Answering (SQuAD v1.1),

- Natural Language Inference (MNLI)

- NER Modeling

#### How to use BERT (Fine-tuning)

Using BERT for a specific task is relatively straightforward:

- BERT can be used for a wide variety of language tasks, while only adding a small layer to the core model:

Classification tasks such as sentiment analysis are done similarly to Next Sentence classification, by adding a classification layer on top of the Transformer output for the [CLS] token.

- In Question Answering tasks (e.g. SQuAD v1.1), the software receives a question regarding a text sequence and is required to mark the answer in the sequence. Using BERT, a Q&A model can be trained by learning two extra vectors that mark the beginning and the end of the answer.

- In Named Entity Recognition (NER), the software receives a text sequence and is required to mark the various types of entities (Person, Organization, Date, etc) that appear in the text. Using BERT, a NER model can be trained by feeding the output vector of each token into a classification layer that predicts the NER label.

#### ROC-Star

- https://github.com/iridiumblue/roc-star/blob/master/README.md

#### LLM

- https://www.manning.com/books/build-a-large-language-model-from-scratch