Instructions to use jamie613/custom_BERT_NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jamie613/custom_BERT_NER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="jamie613/custom_BERT_NER")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("jamie613/custom_BERT_NER") model = AutoModelForTokenClassification.from_pretrained("jamie613/custom_BERT_NER") - Notebooks
- Google Colab
- Kaggle
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## Training and evaluation data
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This model is trained ane evaluated on a custome dataset: [jamie613/custom_NER](https://huggingface.co/datasets/jamie613/custom_NER)
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The set contains 150 samples of concert introductions in Mandarine.
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The dataset is divide into training set (135 samples) and evaluation set (15 samples).
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### Training hyperparameters
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## Training and evaluation data
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This model is trained ane evaluated on a custome dataset: [jamie613/custom_NER](https://huggingface.co/datasets/jamie613/custom_NER)<br>
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The set contains 150 samples of concert introductions in Mandarine.<br>
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The dataset is divide into training set (135 samples) and evaluation set (15 samples).
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### Training hyperparameters
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