Text Classification
Transformers
Safetensors
English
bert
distillation
eamkd
tinybert
text-embeddings-inference
Instructions to use HFTrails/Distilled-Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HFTrails/Distilled-Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HFTrails/Distilled-Model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HFTrails/Distilled-Model") model = AutoModelForSequenceClassification.from_pretrained("HFTrails/Distilled-Model") - Notebooks
- Google Colab
- Kaggle
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---
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language:
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- en
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license: mit
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library_name: transformers
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tags:
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- distillation
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- eamkd
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- tinybert
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## 📢 Project Status
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**The model information and detailed documentation are currently under active update.** Comprehensive model cards will be released progressively. **Stay tuned!**
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*For now, you can refer to our [GitHub Repository](https://github.com/trials032/EAMKD) for the latest code updates.*
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