Text Classification
Transformers
PyTorch
English
bert
text-classfication
int8
Intel® Neural Compressor
PostTrainingStatic
text-embeddings-inference
Instructions to use Intel/bert-base-uncased-CoLA-int8-inc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Intel/bert-base-uncased-CoLA-int8-inc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Intel/bert-base-uncased-CoLA-int8-inc")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Intel/bert-base-uncased-CoLA-int8-inc") model = AutoModelForSequenceClassification.from_pretrained("Intel/bert-base-uncased-CoLA-int8-inc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
language: en
license: mit
tags:
- text-classfication
- int8
- Intel® Neural Compressor
- PostTrainingStatic
- bert
datasets:
- mrpc
- cola
metrics:
- f1
INT8 BERT base uncased finetuned CoLA
Post-training static quantization
PyTorch
This is an INT8 PyTorch model quantized with huggingface/optimum-intel through the usage of Intel® Neural Compressor.
The original fp32 model comes from the fine-tuned model textattack/bert-base-uncased-CoLA.
Test result
| INT8 | FP32 | |
|---|---|---|
| Accuracy (eval-f1) | 0.5451 | 0.5339 |
| Model size (MB) | 112 | 438 |
Load with optimum:
from optimum.intel import INCModelForSequenceClassification
model_id = "Intel/bert-base-uncased-CoLA-int8"
int8_model = INCModelForSequenceClassification.from_pretrained(model_id)