Instructions to use orisuchy/Descriptive_Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use orisuchy/Descriptive_Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="orisuchy/Descriptive_Classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("orisuchy/Descriptive_Classifier") model = AutoModelForSequenceClassification.from_pretrained("orisuchy/Descriptive_Classifier") - Notebooks
- Google Colab
- Kaggle
Update config.json
Browse files- config.json +4 -0
config.json
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"label2id": {
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"Descriptive": 0,
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"Not Descriptive": 1
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},
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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