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Google's Natural Language Processing model with SOT result in various tasks
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README.md

ALBERT for Sentiment Analysis

Dataset preparation

A tab seperated (.tsv) file is required with the name of train i.e. train.tsv Train dataset needs to be placed in a folder.

How to fine-tune

Following parameters are required

  1. --data_dir - Directory where data is stored
  2. --model_type - The model which we wanna use for fine-tuning. Here, we are using albert
  3. --model_name_or_path - The variant of albert which you want to use.
  4. --output_dir - path where you want to save the model.
  5. --do_train - because we are training the model.

Example

$ python run_glue.py --data_dir data --model_type albert --model_name_or_path albert-base-v2 --output_dir output --do_train

Different Models available for use

Average SQuAD1.1 SQuAD2.0 MNLI SST-2 RACE
V2
ALBERT-base 82.3 90.2/83.2 82.1/79.3 84.6 92.9 66.8
ALBERT-large 85.7 91.8/85.2 84.9/81.8 86.5 94.9 75.2
ALBERT-xlarge 87.9 92.9/86.4 87.9/84.1 87.9 95.4 80.7
ALBERT-xxlarge 90.9 94.6/89.1 89.8/86.9 90.6 96.8 86.8
V1
ALBERT-base 80.1 89.3/82.3 80.0/77.1 81.6 90.3 64.0
ALBERT-large 82.4 90.6/83.9 82.3/79.4 83.5 91.7 68.5
ALBERT-xlarge 85.5 92.5/86.1 86.1/83.1 86.4 92.4 74.8
ALBERT-xxlarge 91.0 94.8/89.3 90.2/87.4 90.8 96.9 86.5
(table taken from Google-research)

Prediction

Both docker and python file are available for prediction.

  1. Set the name of folder where model files are stored.
  2. Run api.py file
$ python api.py

or

from api import SentimentAnalyzer
classifier = SentimentAnalyzer()
print(classifier.predict('the movie was nice'))

Thanks to HuggingFace for making the implementation simple and also Google for this awesome pretrained model.

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