Text Classification
Transformers
PyTorch
ONNX
English
distilbert
text-classfication
int8
neural-compressor
Intel® Neural Compressor
PostTrainingStatic
Eval Results (legacy)
text-embeddings-inference
Instructions to use Intel/distilbert-base-uncased-finetuned-sst-2-english-int8-static-inc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Intel/distilbert-base-uncased-finetuned-sst-2-english-int8-static-inc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Intel/distilbert-base-uncased-finetuned-sst-2-english-int8-static-inc")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Intel/distilbert-base-uncased-finetuned-sst-2-english-int8-static-inc") model = AutoModelForSequenceClassification.from_pretrained("Intel/distilbert-base-uncased-finetuned-sst-2-english-int8-static-inc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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language: en
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license: apache-2.0
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INT8 DistilBERT base uncased finetuned SST-2 (Post-training static quantization)
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language: en
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license: apache-2.0
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- text-classfication
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- int8
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- PostTrainingStatic
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datasets:
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- sst2
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metrics:
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INT8 DistilBERT base uncased finetuned SST-2 (Post-training static quantization)
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