Instructions to use interneuronai/customer_feedback_analysis_bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use interneuronai/customer_feedback_analysis_bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="interneuronai/customer_feedback_analysis_bert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("interneuronai/customer_feedback_analysis_bert") model = AutoModelForSequenceClassification.from_pretrained("interneuronai/customer_feedback_analysis_bert") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 55ca0a70cdcb1905d7f30853c5e349166a17c3fb2627662845316affb1d626a1
- Size of remote file:
- 670 MB
- SHA256:
- 2bde6a77b91f1530f36ece9b013ae8e7efe3f825834ac0b7d47df712ed5068b8
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