Token Classification
Transformers
Safetensors
English
roberta
feature-extraction
entity-recognition
foundation-model
RoBERTa
generic
Instructions to use numind/NuNER-v1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use numind/NuNER-v1.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="numind/NuNER-v1.0")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("numind/NuNER-v1.0") model = AutoModel.from_pretrained("numind/NuNER-v1.0") - Notebooks
- Google Colab
- Kaggle
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Read more about evaluation protocol & datasets in our [paper](https://arxiv.org/abs/2402.15343).
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Here is the aggregated performance of the models over several datasets.
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k=X means that as training data for this evaluation, we took only X examples for each class, trained the model, and evaluated it on the full test set.
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Read more about evaluation protocol & datasets in our [paper](https://arxiv.org/abs/2402.15343).
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We suggest using **newer version of this model: [NuNER v2.0](https://huggingface.co/numind/NuNER-v2.0)**
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Here is the aggregated performance of the models over several datasets.
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k=X means that as training data for this evaluation, we took only X examples for each class, trained the model, and evaluated it on the full test set.
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