Text Classification
Transformers
PyTorch
TensorBoard
English
bert
Generated from Trainer
NLP Regression
Regression
Edmunds Car Reviews
text-embeddings-inference
Instructions to use DunnBC22/bert-base-uncased-Regression-Edmunds_Car_Reviews with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DunnBC22/bert-base-uncased-Regression-Edmunds_Car_Reviews with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DunnBC22/bert-base-uncased-Regression-Edmunds_Car_Reviews")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DunnBC22/bert-base-uncased-Regression-Edmunds_Car_Reviews") model = AutoModelForSequenceClassification.from_pretrained("DunnBC22/bert-base-uncased-Regression-Edmunds_Car_Reviews") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 673cc3a9a0e7508d43035cce4dcb1514a9fc9c24eed5da9403c4912e496cea87
- Size of remote file:
- 4.03 kB
- SHA256:
- e317ddff12520db14fb30c0407cb76454f615a415dad6f125b1100c0cd139e8d
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