Instructions to use dnzblgn/Customer-Reviews-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dnzblgn/Customer-Reviews-Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dnzblgn/Customer-Reviews-Classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dnzblgn/Customer-Reviews-Classification") model = AutoModelForSequenceClassification.from_pretrained("dnzblgn/Customer-Reviews-Classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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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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### Categories:
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### label_mapping = {
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pipeline_tag: text-classification
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widget:
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library_name: transformers
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safetensors: true
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### label_mapping = {
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