Text Classification
Transformers
Safetensors
English
bert
pii
privacy
routing
knowledge-distillation
tinybert
traffic-control
Eval Results (legacy)
text-embeddings-inference
Instructions to use pinialt/bert-tiny-pii-router with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pinialt/bert-tiny-pii-router with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pinialt/bert-tiny-pii-router")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pinialt/bert-tiny-pii-router") model = AutoModelForSequenceClassification.from_pretrained("pinialt/bert-tiny-pii-router") - Notebooks
- Google Colab
- Kaggle
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README.md
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This project is licensed under the **Apache License 2.0**.
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Finetuned on Google's bert_uncased_L-4_H-256_A-4 model
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@article{turc2019,
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title={Well-Read Students Learn Better: On the Importance of Pre-training Compact Models},
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author={Turc, Iulia and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
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journal={arXiv preprint arXiv:1908.08962v2 },
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year={2019}
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}
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## Links
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This project is licensed under the **Apache License 2.0**.
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Finetuned on Google's bert_uncased_L-4_H-256_A-4 model
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```
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@article{turc2019,
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title={Well-Read Students Learn Better: On the Importance of Pre-training Compact Models},
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author={Turc, Iulia and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
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journal={arXiv preprint arXiv:1908.08962v2 },
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year={2019}
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}
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```
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## Links
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