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---
language:
- en
thumbnail: url to a thumbnail used in social sharing
tags:
- token classification
license: cc
datasets:
- conll2003
model-index:
- name: sarahmiller137/distilbert-base-uncased-ft-conll2003
results:
- task:
type: token-classification
name: Token Classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: test
metrics:
- name: Accuracy
type: accuracy
value: 0.9750189904012154
verified: true
- name: Precision
type: precision
value: 0.9802152215150602
verified: true
- name: Recall
type: recall
value: 0.9803021169462076
verified: true
- name: F1
type: f1
value: 0.9802586673049137
verified: true
- name: loss
type: loss
value: 0.10723897069692612
verified: true
---
## Model information:
distilibert-base-uncased model finetuned using the conll2003 dataset from the datasets library.
## Intended uses & limitations
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.
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 -
- [CoNLL-2003](https://aclanthology.org/W03-0419)
- [distilbert](https://huggingface.co/distilbert-base-uncased)
## How to use
Load the model from the library using the following checkpoints:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("sarahmiller137/distilbert-base-uncased-ft-conll2003")
model = AutoModel.from_pretrained("sarahmiller137/distilbert-base-uncased-ft-conll2003")
```
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