BERT
Collection
BERT models of varying flavors • 22 items • Updated
How to use Intel/bert-large-uncased-rte-int8-dynamic-inc with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="Intel/bert-large-uncased-rte-int8-dynamic-inc") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Intel/bert-large-uncased-rte-int8-dynamic-inc")
model = AutoModelForSequenceClassification.from_pretrained("Intel/bert-large-uncased-rte-int8-dynamic-inc")This is an INT8 PyTorch model quantized with Intel® Neural Compressor.
The original fp32 model comes from the fine-tuned model yoshitomo-matsubara/bert-large-uncased-rte.
| INT8 | FP32 | |
|---|---|---|
| Accuracy (eval-f1) | 0.7076 | 0.7401 |
| Model size (MB) | 766 | 1349 |
from optimum.intel import INCModelForSequenceClassification
model_id = "Intel/bert-large-uncased-rte-int8-dynamic"
int8_model = INCModelForSequenceClassification.from_pretrained(model_id)