Text Classification
Transformers
PyTorch
English
bert
rte
glue
torchdistill
nlp
int8
neural-compressor
Intel® Neural Compressor
text-classfication
PostTrainingDynamic
text-embeddings-inference
Instructions to use Intel/bert-large-uncased-rte-int8-dynamic-inc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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")# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Intel/bert-large-uncased-rte-int8-dynamic-inc: direct link, hf CLI and curl.
- Browser
- Download file 766 MB
-
https://huggingface.co/Intel/bert-large-uncased-rte-int8-dynamic-inc/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Intel/bert-large-uncased-rte-int8-dynamic-inc/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Intel/bert-large-uncased-rte-int8-dynamic-inc/resolve/main/pytorch_model.bin
766 MB
- Xet hash:
- 0cb28d4b2d9f0c4a2ea8ecbfced2dcb56ec394b4b79f23a6deb9876893d1d367
- Size of remote file:
- 766 MB
- SHA256:
- efd297a2f3d1642a185d4b278dd6c415489350b23dfed54a2659981b0c806a33
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