Open area for investigation:
The model parameters are marked trainable, so it should be possible to fine-tune on our own COVID tweet dataset.
Alternatively, explore fine tuning other models such as BERT on a semantic similarity task as done here: Sentence-BERT
Comparison of base pre-trained vs. fine-tuned Universal Sentence Encoder (USE) can be done quantitatively or qualitatively , see #1. Same goes for comparing USE vs. BERT or any other model.
Currently we are using the pre-trained Universal Sentence Encoder (large) from TensorFlow hub.
Open area for investigation: The model parameters are marked trainable, so it should be possible to fine-tune on our own COVID tweet dataset.
Alternatively, explore fine tuning other models such as BERT on a semantic similarity task as done here: Sentence-BERT
Comparison of base pre-trained vs. fine-tuned Universal Sentence Encoder (USE) can be done quantitatively or qualitatively , see #1. Same goes for comparing USE vs. BERT or any other model.