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
add labels names
Browse files- config.json +3 -3
config.json
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@@ -10,9 +10,9 @@
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"id2label": {
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"0": "
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"1": "
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"2": "
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"id2label": {
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"0": "Descriptive",
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"1": "Might Descriptive",
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"2": "Not Descriptive"
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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