Text Classification
Transformers
TensorFlow
bert
generated_from_keras_callback
text-embeddings-inference
Instructions to use codingtree/bert-base-nsmc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use codingtree/bert-base-nsmc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="codingtree/bert-base-nsmc")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("codingtree/bert-base-nsmc") model = AutoModelForSequenceClassification.from_pretrained("codingtree/bert-base-nsmc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fb98cdcb3b4531368446986cd92027da3987620d7a9c006ca043fec8c82eaccc
- Size of remote file:
- 443 MB
- SHA256:
- 71bc1e72e07e1db4ae272e5a4efeaf288a3fbec2b516d069787b66a69da90c33
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