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--- |
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language: |
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- en |
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thumbnail: url to a thumbnail used in social sharing |
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tags: |
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- token classification |
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license: cc |
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datasets: |
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- conll2003 |
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model-index: |
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- name: sarahmiller137/distilbert-base-uncased-ft-conll2003 |
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results: |
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- task: |
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type: token-classification |
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name: Token Classification |
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dataset: |
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name: conll2003 |
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type: conll2003 |
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config: conll2003 |
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split: test |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9750189904012154 |
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verified: true |
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- name: Precision |
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type: precision |
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value: 0.9802152215150602 |
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verified: true |
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- name: Recall |
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type: recall |
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value: 0.9803021169462076 |
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verified: true |
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- name: F1 |
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type: f1 |
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value: 0.9802586673049137 |
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verified: true |
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- name: loss |
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type: loss |
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value: 0.10723897069692612 |
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verified: true |
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--- |
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## Model information: |
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distilibert-base-uncased model finetuned using the conll2003 dataset from the datasets library. |
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## Intended uses & limitations |
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This model is intended to be used for named entity recoginition tasks. The model will identify entities of persons, locations, organisations, and miscellaneous. The model will predict lables based upon the CoNLL-2003 dataset. |
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Note that the dataset and model may not be fully represetative or suitable for all needs it is recommended that the paper for the dataset and base model card should be reviewed before using the model - |
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- [Conll2003](https://aclanthology.org/W03-0419) |
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- [distilbert](https://huggingface.co/distilbert-base-uncased) |
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## How to use |
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Load the model from the library using the following checkpoints: |
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```python |
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from transformers import AutoTokenizer, AutoModel |
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tokenizer = AutoTokenizer.from_pretrained("sarahmiller137/distilbert-base-uncased-ft-conll2003") |
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model = AutoModel.from_pretrained("sarahmiller137/distilbert-base-uncased-ft-conll2003") |
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``` |
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