Model Description
FinABSA is a T5-Large model trained for Aspect-Based Sentiment Analysis(ABSA) tasks using SEntFiN 1.0. Unlike traditional sentiment analysis models which predict a single sentiment label for each sentence, FinABSA has been trained to disambiguate sentences containing multiple aspects. 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(output)
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(output)
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%.
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