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
File size: 472 Bytes
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language:
- en
license: mit
library_name: transformers
tags:
- distillation
- eamkd
- tinybert
---
## 📢 Project Status
**The model information and detailed documentation are currently under active update.** Comprehensive model cards will be released progressively. **Stay tuned!**
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*This is a distilled model trained on the LH dataset. The one trained on the HX dataset can be found at [Distilled-Model-HX](https://huggingface.co/HFTrails/Distilled-Model-HX).* |