readme: ---
  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
  
  ```python
  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}]
  """
  
  ```