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.
- --data_dir - Directory where data is stored
- --model_type - The model which we wanna use for fine-tuning. Here, we are using albert
- --model_name_or_path - The variant of albert which you want to use.
- --output_dir - path where you want to save the model.
- --do_train - because we are training the model.
$ python run_glue.py --data_dir data --model_type albert --model_name_or_path albert-base-v2 --output_dir output --do_train
| 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) |
Both docker and python file are available for prediction.
- Set the name of folder where model files are stored.
- Run api.py file
$ python api.py
or
from api import SentimentAnalyzer
classifier = SentimentAnalyzer()
print(classifier.predict('the movie was nice'))
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