Text Classification
Transformers
Safetensors
English
bert
bert-base-uncased
text-embeddings-inference
Instructions to use disham993/electrical-classification-bert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use disham993/electrical-classification-bert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="disham993/electrical-classification-bert-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("disham993/electrical-classification-bert-base") model = AutoModelForSequenceClassification.from_pretrained("disham993/electrical-classification-bert-base") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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## Last update
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## Last update
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## Citation
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```
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@misc{modernbert,
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title={Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference},
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author={Benjamin Warner and Antoine Chaffin and Benjamin Clavié and Orion Weller and Oskar Hallström and Said Taghadouini and Alexis Gallagher and Raja Biswas and Faisal Ladhak and Tom Aarsen and Nathan Cooper and Griffin Adams and Jeremy Howard and Iacopo Poli},
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year={2024},
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eprint={2412.13663},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2412.13663},
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}
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```
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