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
Update config.json
Browse files- config.json +1 -1
config.json
CHANGED
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"seq_classif_dropout": 0.2,
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"sinusoidal_pos_embds": false,
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"tie_weights_": true,
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"torch_dtype": "
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"transformers_version": "4.17.0",
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"vocab_size": 30522
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}
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"seq_classif_dropout": 0.2,
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"sinusoidal_pos_embds": false,
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"tie_weights_": true,
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+
"torch_dtype": "int8",
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"transformers_version": "4.17.0",
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"vocab_size": 30522
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}
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