Text Classification
Transformers
PyTorch
ONNX
English
albert
text-classfication
int8
Intel® Neural Compressor
neural-compressor
PostTrainingStatic
Instructions to use INC4AI/albert-base-v2-sst2-int8-static-inc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use INC4AI/albert-base-v2-sst2-int8-static-inc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="INC4AI/albert-base-v2-sst2-int8-static-inc")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("INC4AI/albert-base-v2-sst2-int8-static-inc") model = AutoModelForSequenceClassification.from_pretrained("INC4AI/albert-base-v2-sst2-int8-static-inc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from INC4AI/albert-base-v2-sst2-int8-static-inc: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/INC4AI/albert-base-v2-sst2-int8-static-inc/resolve/d25bbaae0a0d5cbbb46af78e9bedaea5590cc787/pytorch_model.bin
- Command line
-
hf download hf://INC4AI/albert-base-v2-sst2-int8-static-inc@d25bbaae0a0d5cbbb46af78e9bedaea5590cc787/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/INC4AI/albert-base-v2-sst2-int8-static-inc/resolve/d25bbaae0a0d5cbbb46af78e9bedaea5590cc787/pytorch_model.bin
438 MB
- Xet hash:
- 9826a390342ce983844355c2b600d6fe491b0555b7915bacafe1f38f6995afbb
- Size of remote file:
- 438 MB
- SHA256:
- 6e4730f4d5dae798ea97c3831fb54c53e13c5ab93369b7a0e4acf144f2ecba2b
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