Text Classification
Transformers
PyTorch
English
distilbert
text-classfication
nlp
neural-compressor
PostTrainingDynamic
int8
Intel® Neural Compressor
text-embeddings-inference
Instructions to use Intel/distilbert-base-uncased-MRPC-int8-dynamic-inc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Intel/distilbert-base-uncased-MRPC-int8-dynamic-inc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Intel/distilbert-base-uncased-MRPC-int8-dynamic-inc")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Intel/distilbert-base-uncased-MRPC-int8-dynamic-inc") model = AutoModelForSequenceClassification.from_pretrained("Intel/distilbert-base-uncased-MRPC-int8-dynamic-inc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
update loading instructions
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README.md
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To load the quantized model, you can do as follows:
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```python
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from optimum.intel
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```
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#### Test result
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To load the quantized model, you can do as follows:
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```python
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from optimum.intel import INCModelForSequenceClassification
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model_id = "Intel/distilbert-base-uncased-MRPC-int8-dynamic"
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model = INCModelForSequenceClassification.from_pretrained(model_id)
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```
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#### Test result
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