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")# pip install -U transformers accelerate # 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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| **Throughput (samples/sec)** |47.554|23.046|
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| **Accuracy (eval-accuracy)** |0.9037|0.9106|
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### Load with nlp-toolkit:
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| **Throughput (samples/sec)** |47.554|23.046|
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| **Accuracy (eval-accuracy)** |0.9037|0.9106|
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| **Model size (MB)** |65|255|
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### Load with nlp-toolkit:
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