Text Classification
Transformers
PyTorch
English
BertABSAForSequenceClassification
aspect-term-sentiment-analysis
ATSA
custom_code
Instructions to use tezign/BERT-LSTM-based-ABSA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tezign/BERT-LSTM-based-ABSA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tezign/BERT-LSTM-based-ABSA", trust_remote_code=True)# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("tezign/BERT-LSTM-based-ABSA", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b5e82d3cffd917a23b66ae0cfeced628cca681d26c4d3a7a31833a10df2dd9c6
- Size of remote file:
- 440 MB
- SHA256:
- cb67cfc95d551bc3ae74ba297ab0c59e62d3c9ee53154999601576a856cfa9f2
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