Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use genaibook/classifier-chapter4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use genaibook/classifier-chapter4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="genaibook/classifier-chapter4")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("genaibook/classifier-chapter4") model = AutoModelForSequenceClassification.from_pretrained("genaibook/classifier-chapter4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update config.json
Browse files- config.json +4 -4
config.json
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"initializer_range": 0.02,
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"label2id": {
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"max_position_embeddings": 512,
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"model_type": "distilbert",
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},
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"initializer_range": 0.02,
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"label2id": {
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"World": 0,
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"Sports": 1,
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"Business": 2,
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"Sci/Tech": 3
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"max_position_embeddings": 512,
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"model_type": "distilbert",
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