
# 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}]
"""

```
{"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] "}]}
transformers pytorch BertABSAForSequenceClassification text-classification aspect-term-sentiment-analysis ATSA custom_code en dataset:semeval2014 arxiv:2002.04815 autotrain_compatible region:us
TAGS
#transformers #pytorch #BertABSAForSequenceClassification #text-classification #aspect-term-sentiment-analysis #ATSA #custom_code #en #dataset-semeval2014 #arxiv-2002.04815 #autotrain_compatible #region-us 


# Note

'Aspect term sentiment analysis'

BERT LSTM based baseline, based on URL *BERT LSTM* implementation.The model trained on SemEval2014-Task 4 laptop and restaurant datasets.

Our Github repo: URL

Code for the paper "Utilizing BERT Intermediate Layers for Aspect Based Sentiment Analysis and Natural Language Inference" URL

# Usage


# Note

'Aspect term sentiment analysis'

BERT LSTM based baseline, based on URL *BERT LSTM* implementation.The model trained on SemEval2014-Task 4 laptop and restaurant datasets.

Our Github repo: URL

Code for the paper "Utilizing BERT Intermediate Layers for Aspect Based Sentiment Analysis and Natural Language Inference" URL # Usage
TAGS
#transformers #pytorch #BertABSAForSequenceClassification #text-classification #aspect-term-sentiment-analysis #ATSA #custom_code #en #dataset-semeval2014 #arxiv-2002.04815 #autotrain_compatible #region-us 
 # Note

'Aspect term sentiment analysis'

BERT LSTM based baseline, based on URL *BERT LSTM* implementation.The model trained on SemEval2014-Task 4 laptop and restaurant datasets.

Our Github repo: URL

Code for the paper "Utilizing BERT Intermediate Layers for Aspect Based Sentiment Analysis and Natural Language Inference" URL # Usage
passage: TAGS
#transformers #pytorch #BertABSAForSequenceClassification #text-classification #aspect-term-sentiment-analysis #ATSA #custom_code #en #dataset-semeval2014 #arxiv-2002.04815 #autotrain_compatible #region-us 
# Note

'Aspect term sentiment analysis'

BERT LSTM based baseline, based on URL *BERT LSTM* implementation.The model trained on SemEval2014-Task 4 laptop and restaurant datasets.

Our Github repo: URL

Code for the paper "Utilizing BERT Intermediate Layers for Aspect Based Sentiment Analysis and Natural Language Inference" URL# Usage