Text Classification
Transformers
PyTorch
ONNX
English
bert
text-classfication
int8
Intel® Neural Compressor
neural-compressor
PostTrainingStatic
text-embeddings-inference
Instructions to use INC4AI/bert-base-uncased-mrpc-int8-static-inc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use INC4AI/bert-base-uncased-mrpc-int8-static-inc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="INC4AI/bert-base-uncased-mrpc-int8-static-inc")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("INC4AI/bert-base-uncased-mrpc-int8-static-inc") model = AutoModelForSequenceClassification.from_pretrained("INC4AI/bert-base-uncased-mrpc-int8-static-inc") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c741dd893629832d3049c5686b6249ca3ff3208417bbdce203e96e461d01c69a
- Size of remote file:
- 247 MB
- SHA256:
- 95d79d0083517ba6280f566add4624c6533eddf5e2d156d3bec64d0d049af637
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