Instructions to use nikhilkumarnk/bert_productrreviews_sentimentanalysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nikhilkumarnk/bert_productrreviews_sentimentanalysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nikhilkumarnk/bert_productrreviews_sentimentanalysis")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nikhilkumarnk/bert_productrreviews_sentimentanalysis") model = AutoModelForSequenceClassification.from_pretrained("nikhilkumarnk/bert_productrreviews_sentimentanalysis") - Notebooks
- Google Colab
- Kaggle
Model Card for Model ID
Model Details
Model Description
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by: Nikhil Kumar
- Model type: BERT
- Language(s) (NLP): NLP
Training Details
Training Data
McAuley-Lab/Amazon-Reviews-2023
Results
Accuracy : 91.51%
Summary
Can be used to understand the user product review
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