Instructions to use dnzblgn/Sentiment-Analysis-Customer-Reviews with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dnzblgn/Sentiment-Analysis-Customer-Reviews with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dnzblgn/Sentiment-Analysis-Customer-Reviews")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dnzblgn/Sentiment-Analysis-Customer-Reviews") model = AutoModelForSequenceClassification.from_pretrained("dnzblgn/Sentiment-Analysis-Customer-Reviews") - Notebooks
- Google Colab
- Kaggle
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base_model:
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- microsoft/deberta-v3-base
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pipeline_tag: text-classification
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---
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### Training Details
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base_model:
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- microsoft/deberta-v3-base
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pipeline_tag: text-classification
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widget:
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- text: The product arrived on time and was exactly as described.
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library_name: transformers
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safetensors: true
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### Training Details
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