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README.md
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## About the Model
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An English Named Entity Recognition model, trained on Maccrobat to recognize the bio-medical entities (107 entities) from a given text corpus (case reports etc.). This model was built on top of distilbert-base-uncased
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- Dataset
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- Carbon emission
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- Training time
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- GPU used : 1 x GeForce RTX 3060 Laptop GPU
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## Usage
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The easiest way is to load the inference api from huggingface and second method is through the pipeline object offered by transformers library.
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```python
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## About the Model
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An English Named Entity Recognition model, trained on Maccrobat to recognize the bio-medical entities (107 entities) from a given text corpus (case reports etc.). This model was built on top of distilbert-base-uncased
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- Dataset: Maccrobat https://figshare.com/articles/dataset/MACCROBAT2018/9764942
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- Carbon emission: 0.0279399890043426 Kg
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- Training time: 30.16527 minutes
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- GPU used : 1 x GeForce RTX 3060 Laptop GPU
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Checkout the tutorial video for explanation of this model and corresponding python library: https://youtu.be/xpiDPdBpS18
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## Usage
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The easiest way is to load the inference api from huggingface and second method is through the pipeline object offered by transformers library.
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```python
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