Instructions to use slamos/bc-models-deberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use slamos/bc-models-deberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="slamos/bc-models-deberta")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("slamos/bc-models-deberta") model = AutoModelForSequenceClassification.from_pretrained("slamos/bc-models-deberta") - Notebooks
- Google Colab
- Kaggle
Upload train_config.json with huggingface_hub
Browse files- train_config.json +3 -3
train_config.json
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{
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"model_type": "deberta",
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"pretrained": "microsoft/deberta-v3-base",
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"epochs":
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"batch_size": 16,
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"lr": 1e-05,
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"max_length": 512,
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"class_weights": [
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1.0,
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],
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"num_labels": 3,
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"id2label": {
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{
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"model_type": "deberta",
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"pretrained": "microsoft/deberta-v3-base",
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"epochs": 5,
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"batch_size": 16,
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"lr": 1e-05,
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"max_length": 512,
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"class_weights": [
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1.0,
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3.5319032669067383,
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10.121383666992188
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],
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"num_labels": 3,
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"id2label": {
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