README.md

---
license: apache-2.0
base_model: distilbert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-NER
  results: []
---
# distilbert-NER

## Model description

**distilbert-NER** is the fine-tuned version of **DistilBERT**, which is a distilled variant of the BERT model. DistilBERT has fewer parameters than BERT, making it smaller, faster, and more efficient. distilbert-NER is specifically fine-tuned for the task of **Named Entity Recognition (NER)**.

This model accurately identifies the same four types of entities as its BERT counterparts: location (LOC), organizations (ORG), person (PER), and Miscellaneous (MISC). Although it is a more compact model, distilbert-NER demonstrates a robust performance in NER tasks, balancing between size, speed, and accuracy.

The model was fine-tuned on the English version of the [CoNLL-2003 Named Entity Recognition](https://www.aclweb.org/anthology/W03-0419.pdf) dataset, which is widely recognized for its comprehensive and diverse range of entity types.

### Available NER models 
| Model Name | Description | Parameters |
|-------------------|-------------|------------------|
| [bert-base-NER](https://huggingface.co/dslim/bert-base-NER) | Fine-tuned BERT-base model for NER - balanced performance | 110M |
| [distilbert-NER](https://huggingface.co/dslim/distilbert-NER) | Fine-tuned DistilBERT - smaller, faster, lighter than BERT | 66M |

## Intended uses & limitations

#### How to use

This model can be utilized with the Transformers *pipeline* for NER, similar to the BERT models.

```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline

tokenizer = AutoTokenizer.from_pretrained("dslim/distilbert-NER")
model = AutoModelForTokenClassification.from_pretrained("dslim/distilbert-NER")

nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "My name is Wolfgang and I live in Berlin"

ner_results = nlp(example)
print(ner_results)
```

#### Limitations and bias

The performance of distilbert-NER is linked to its training on the CoNLL-2003 dataset. Therefore, it might show limited effectiveness on text data that significantly differs from this training set. Users should be aware of potential biases inherent in the training data and the possibility of entity misclassification in complex sentences.

## Training data

The model was fine-tuned on the English version of the standard [CoNLL-2003 Named Entity Recognition](https://www.aclweb.org/anthology/W03-0419.pdf) dataset, known for its effectiveness in training NER models.

## Training procedure

The training details, including hardware specifications, aren't specified. However, the model's training followed the best practices suitable for distilbert models, aiming at an efficient balance between learning efficiency and model accuracy.

## Eval results
| Metric     | Score |
|------------|-------|
| Loss       | 0.0710|
| Precision  | 0.9202|
| Recall     | 0.9232|
| F1         | 0.9217|
| Accuracy   | 0.9810|

The training and validation losses demonstrate a decrease over epochs, signaling effective learning. The precision, recall, and F1 scores are competitive, showcasing the model's robustness in NER tasks.

### BibTeX entry and citation info

For DistilBERT:

```
@article{sanh2019distilbert,
  title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter},
  author={Sanh, Victor and Debut, Lysandre and Chaumond, Julien and Wolf, Thomas},
  journal={arXiv preprint arXiv:1910.01108},
  year={2019}
}
```

For the underlying BERT model:

```
@article{DBLP:journals/corr/abs-1810-04805,
  author    = {Jacob Devlin and
               Ming{-}Wei Chang and
               Kenton Lee and
               Kristina Toutanova},
  title     = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language
               Understanding},
  journal   = {CoRR},
  volume    = {abs/1810.04805},
  year      = {2018},
  url       = {http://arxiv.org/abs/1810.04805},
  archivePrefix = {arXiv},
  eprint    = {1810.04805},
  timestamp = {Tue, 30 Oct 2018 20:39:56 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib},
  bibsource = {db

lp computer science bibliography, https://dblp.org}
}
```