Text Classification
Transformers
PyTorch
Core ML
Safetensors
English
distilbert
text-embeddings-inference
Instructions to use Falconsai/intent_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Falconsai/intent_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Falconsai/intent_classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Falconsai/intent_classification") model = AutoModelForSequenceClassification.from_pretrained("Falconsai/intent_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): 1f63525
Upload config.json
Browse files- config.json +1 -2
config.json
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{
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"activation": "gelu",
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"architectures": [
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"DistilBertForSequenceClassification"
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"26": "track refund"
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"initializer_range": 0.02,
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"label2id": null,
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"max_position_embeddings": 512,
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"model_type": "distilbert",
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"n_heads": 12,
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{
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"_name_or_path": "Falconsai/intent_classification",
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"activation": "gelu",
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"architectures": [
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"DistilBertForSequenceClassification"
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"26": "track refund"
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},
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"initializer_range": 0.02,
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"max_position_embeddings": 512,
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"model_type": "distilbert",
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"n_heads": 12,
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