Text Classification
Transformers
ONNX
English
roberta
text-classfication
int8
Intel® Neural Compressor
PostTrainingDynamic
Instructions to use Intel/roberta-base-mrpc-int8-dynamic-inc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Intel/roberta-base-mrpc-int8-dynamic-inc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Intel/roberta-base-mrpc-int8-dynamic-inc")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Intel/roberta-base-mrpc-int8-dynamic-inc") model = AutoModelForSequenceClassification.from_pretrained("Intel/roberta-base-mrpc-int8-dynamic-inc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
language: en
license: mit
tags:
- text-classfication
- int8
- Intel® Neural Compressor
- PostTrainingDynamic
- onnx
datasets:
- mrpc
metrics:
- f1
INT8 roberta base finetuned MRPC
Post-training dynamic quantization
ONNX
This is an INT8 ONNX model quantized with Intel® Neural Compressor.
The original fp32 model comes from the fine-tuned model Intel/roberta-base-mrpc.
Test result
| INT8 | FP32 | |
|---|---|---|
| Accuracy (eval-f1) | 0.9085 | 0.9138 |
| Model size (MB) | 122 | 476 |
Load ONNX model:
from optimum.onnxruntime import ORTModelForSequenceClassification
model = ORTModelForSequenceClassification.from_pretrained('Intel/roberta-base-mrpc-int8-dynamic')