Text Classification
Transformers
PyTorch
English
bert
text-classfication
int8
Intel® Neural Compressor
QuantizationAwareTraining
text-embeddings-inference
Instructions to use INC4AI/bert-base-uncased-mrpc-int8-qat-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-qat-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-qat-inc")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("INC4AI/bert-base-uncased-mrpc-int8-qat-inc") model = AutoModelForSequenceClassification.from_pretrained("INC4AI/bert-base-uncased-mrpc-int8-qat-inc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from INC4AI/bert-base-uncased-mrpc-int8-qat-inc: direct link, hf CLI and curl.
- Browser
- Download file 112 MB
-
https://huggingface.co/INC4AI/bert-base-uncased-mrpc-int8-qat-inc/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://INC4AI/bert-base-uncased-mrpc-int8-qat-inc/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/INC4AI/bert-base-uncased-mrpc-int8-qat-inc/resolve/main/pytorch_model.bin
112 MB
- Xet hash:
- f6dad63c2fa5dba499414fa24039a29cf91bafb0869754ae4ad1db7787bac48f
- Size of remote file:
- 112 MB
- SHA256:
- 6ecca80b43ac43958770200779935ad1df27a8b190049429e59daecd988bac93
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