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An assignment with the goal of classifying named entities in using LSTMs and word embeddings.

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auNLP/assignment4-NER-using-LSTMs

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See assignment_description.md for a description of the assignment

Summary

Include a short description of which models you have tried and conclusions from comparing these models. This should be no longer than an abstract. This section can also include questions regarding the assignment.

Performance

This should include a table of performance metrics of different models. The performance metrics should at least include accuracy and F1-score.

Project Organization

The organization of the project is as follows:

Correct this to reflect any changes you make

├── LICENSE                    <- the license of this code
├── README.md                  <- The top-level README for this project.
├── .github            
│   └── workflows              <- workflows to automatically run when code is pushed
│   │    └── pytest.yml        <- A workflow which runs pytests upon push
├── classification             <- The main folder for scripts
│   ├── tests                  <- The pytest test suite
│   │   └── ...
|   └── ...
├── .gitignore                 <- A list of files not uploaded to git
├── requirement.txt            <- A requirements file of the required packages.
└── assignment_description.md  <- the assignment description

Running the code

You can run the reproduce all the experiments by cloning the GitHub repository and running the following:

Update the code below to demonstrate how your code should be run to produce the results in the result section. E.g.:

pip install -r requirements.txt
python ner/main.py --epochs 10 --gensim_embedding glove-wiki-gigaword-50

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An assignment with the goal of classifying named entities in using LSTMs and word embeddings.

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