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")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Falconsai/intent_classification") model = AutoModelForSequenceClassification.from_pretrained("Falconsai/intent_classification") - Notebooks
- Google Colab
- Kaggle
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README.md
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- Evaluation Samples per Second: 1726.29
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- Evaluation Steps per Second: 215.826
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**Note:** Specific evaluation statistics should be provided based on the model's performance.
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## Responsible Usage
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It is essential to use this model responsibly and ethically, adhering to content guidelines and applicable regulations when implementing it in real-world applications, particularly those involving potentially sensitive content.
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- Evaluation Samples per Second: 1726.29
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- Evaluation Steps per Second: 215.826
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## Responsible Usage
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It is essential to use this model responsibly and ethically, adhering to content guidelines and applicable regulations when implementing it in real-world applications, particularly those involving potentially sensitive content.
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