Text Classification
Transformers
Safetensors
English
distilbert
sentiment-analysis
sentiment
synthetic data
multi-class
social-media-analysis
customer-feedback
product-reviews
brand-monitoring
text-embeddings-inference
Instructions to use tabularisai/robust-sentiment-analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tabularisai/robust-sentiment-analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tabularisai/robust-sentiment-analysis")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tabularisai/robust-sentiment-analysis") model = AutoModelForSequenceClassification.from_pretrained("tabularisai/robust-sentiment-analysis") - Notebooks
- Google Colab
- Kaggle
Vadim Borisov commited on
Upload quantize_config.json with huggingface_hub
Browse files- quantize_config.json +32 -0
quantize_config.json
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{
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"per_channel": true,
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"reduce_range": true,
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"per_model_config": {
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"model": {
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"op_types": [
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"Reshape",
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"Sqrt",
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"Tanh",
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"Mul",
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"Concat",
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"Add",
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"ReduceMean",
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"Cast",
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"Sub",
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"Erf",
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"MatMul",
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"Unsqueeze",
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"Transpose",
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"Constant",
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"Gemm",
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"Shape",
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"Softmax",
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"Gather",
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"Div",
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"Pow"
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],
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"weight_type": "QInt8"
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}
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}
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}
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