Text Classification
Transformers
PyTorch
English
bert
text-classfication
int8
Intel® Neural Compressor
PostTrainingStatic
text-embeddings-inference
Instructions to use Intel/bert-base-uncased-STS-B-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-STS-B-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-STS-B-int8-inc")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Intel/bert-base-uncased-STS-B-int8-inc") model = AutoModelForSequenceClassification.from_pretrained("Intel/bert-base-uncased-STS-B-int8-inc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| language: en | |
| license: mit | |
| tags: | |
| - text-classfication | |
| - int8 | |
| - Intel® Neural Compressor | |
| - PostTrainingStatic | |
| - bert | |
| datasets: | |
| - mrpc | |
| - stsb | |
| metrics: | |
| - f1 | |
| # INT8 BERT base uncased finetuned STS-B | |
| ## Post-training static quantization | |
| ### PyTorch | |
| This is an INT8 PyTorch model quantized with [huggingface/optimum-intel](https://github.com/huggingface/optimum-intel) through the usage of [Intel® Neural Compressor](https://github.com/intel/neural-compressor). | |
| The original fp32 model comes from the fine-tuned model [textattack/bert-base-uncased-STS-B](https://huggingface.co/textattack/bert-base-uncased-STS-B). | |
| #### Test result | |
| | |INT8|FP32| | |
| |---|:---:|:---:| | |
| | **Accuracy (eval-f1)** |0.8755|0.8805| | |
| | **Model size (MB)** |118|438| | |
| #### Load with optimum: | |
| ```python | |
| from optimum.intel import INCModelForSequenceClassification | |
| model_id = "Intel/bert-base-uncased-STS-B-int8" | |
| int8_model = INCModelForSequenceClassification.from_pretrained(model_id) | |
| ``` | |