metadata

language: en
tags:
  - aspect-term-sentiment-analysis
  - pytorch
  - ATSA
datasets:
  - semeval2014
widget:
  - text: >-
      [CLS] The appearance is very nice, but the battery life is poor. [SEP]
      appearance [SEP]

Note

Aspect term sentiment analysis

BERT LSTM based baseline, based on https://github.com/avinashsai/BERT-Aspect BERT LSTM implementation.The model trained on SemEval2014-Task 4 laptop and restaurant datasets.

Our Github repo: https://github.com/tezignlab/BERT-LSTM-based-ABSA

Code for the paper "Utilizing BERT Intermediate Layers for Aspect Based Sentiment Analysis and Natural Language Inference" https://arxiv.org/pdf/2002.04815.pdf.

Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline

MODEL = "tezign/BERT-LSTM-based-ABSA"

tokenizer = AutoTokenizer.from_pretrained(MODEL)

model = AutoModelForSequenceClassification.from_pretrained(MODEL, trust_remote_code=True)

classifier = TextClassificationPipeline(model=model, tokenizer=tokenizer)

result = classifier([
    {"text": "The appearance is very nice, but the battery life is poor", "text_pair": "appearance"},
    {"text": "The appearance is very nice, but the battery life is poor", "text_pair": "battery"}
],
    function_to_apply="softmax")

print(result)

"""
print result
>> [{'label': 'positive', 'score': 0.9129462838172913}, {'label': 'negative', 'score': 0.8834680914878845}]
"""