NLP tool to extract emotional phrase from tweets 🤩

Related tags

Text Data & NLPnlpdockerextractionsentimentpytorchhuggingface
Overview

Emotional phrase extractor

Extract phrase in the given text that is used to express the sentiment. Capturing sentiment in language is important in these times where decisions and reactions are created and updated in seconds. But, which words actually lead to the sentiment description? This project aims to solve this problem.

Powered using Pytorch + hugggingface 🤗

Try it out.

git clone https://github.com/shahules786/twitter-emotions.git

cd twitter-emotions

sudo docker build --tag twitter-emotions:api .

sudo docker run -p 9999:9999  -it twitter-emotions:api python twitteremotions/app.py

Server will start running on port 9999 of localhost

Example

Installation for development

git clone https://github.com/shahules786/twitter-emotions.git

cd twitter-emotions

pip install -r requirements.txt

Train Model on your data

from twitteremotions.emotions import TwitterEmotions
emotions = TwitterEmotions()
emotions.train(train_path="data/train.csv", epochs=10, batch_size=32, max_len=168, test_size=0.25)

Contributing

All contrbutions are welcome 👋

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Comments
  • avoid confusion : end_tokens instead of start_tokens

    avoid confusion : end_tokens instead of start_tokens

    Avoid Confusion

    Replace start_tokens with end_tokens for the fourth argument to calculate the loss function to avoid confusion :)

    While reviewing your amazing project, I noticed that the EmotionData class of the dataloader.py file is returning:

    {
    ...
   # start_tokens
   "start_tokens": torch.tensor(start_tokens, dtype=torch.long),
   # end_tokens
   "end_tokens": torch.tensor(end_tokens, dtype=torch.long),
}

    But in the engine.py file you are passing start_tokens for both the third and fourth arguments of the loss_fn():

    loss = loss_fn(
            start, end, torch.argmax(data["start_tokens"], axis=1), torch.argmax(data["start_tokens"], axis=1)
        )

    But the fourth has to be end_tokens. This minor change will not affect the loss_fn() output function since they are equal in all cases [=1].But, to respect conventions and avoid confusion, it would be better if it looks like the one shown below on the right:

    image

    opened by zekaouinoureddine 0
Releases(v1.0.0)
Owner
Shahul ES
Data Scientist | Kaggle GrandMaster ( Rank 20) | Opensource @mljar
Shahul ES
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