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
- Xet hash:
- 52c5c8b2e7176c63a7c069f25095226357300c415f8c4c9244323374ab8ef3a7
- Size of remote file:
- 26.2 MB
- SHA256:
- 71529305e5145361f466d12f8bbb8b642d16cc1fe37df959c12dc560b0ea7571
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