amphora / FinABSA-Longer

Model Description

FinABSA-Longer is a T5-Large model trained for Aspect-Based Sentiment Analysis(ABSA) tasks using SEntFiN 1.0 and additional augmentation techniques. It shows robust behavior to even longer sequences compared to the previous FinABSA model . By replacing the target aspect with a [TGT] token the model predicts the sentiment concentrating to the aspect. GitHub Repo

How to use

You can use this model directly using the AutoModelForSeq2SeqLM class.

>>> from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

>>> tokenizer = AutoTokenizer.from_pretrained("amphora/FinABSA")
>>> model = AutoModelForSeq2SeqLM.from_pretrained("amphora/FinABSA")

>>> input_str = "[TGT] stocks dropped 42% while Samsung rallied."
>>> input = tokenizer(input_str, return_tensors='pt')
>>> output = model.generate(**input, max_length=20)
>>> print(tokenizer.decode(output[0]))
The sentiment for [TGT] in the given sentence is NEGATIVE.
>>> input_str = "Tesla stocks dropped 42% while [TGT] rallied."
>>> input = tokenizer(input_str, return_tensors='pt')
>>> output = model.generate(**input, max_length=20)
>>> print(tokenizer.decode(output[0]))
The sentiment for [TGT] in the given sentence is POSITIVE.

Evaluation Results

Using a test split arbitarly extracted from SEntFiN 1.0 the model scores an average accuracy of 87%.