---
license: apache-2.0
tags:
- token-classification
datasets:
- wikiann-conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilroberta-base-ner-wikiann-conll2003-3-class
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wikiann-conll2003
type: wikiann-conll2003
metrics:
- name: Precision
type: precision
value: 0.9624757386241104
- name: Recall
type: recall
value: 0.9667497021553124
- name: F1
type: f1
value: 0.964607986167396
- name: Accuracy
type: accuracy
value: 0.9913626461292995
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilroberta-base-ner-wikiann-conll2003-3-class
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the wikiann and conll2003 dataset. It consists out of the classes of wikiann.
O (0), B-PER (1), I-PER (2), B-ORG (3), I-ORG (4) B-LOC (5), I-LOC (6).
eval F1-Score: **96,25** (merged dataset)
test F1-Score: **92,41** (merged dataset)
## Model Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("philschmid/distilroberta-base-ner-wikiann-conll2003-3-class")
model = AutoModelForTokenClassification.from_pretrained("philschmid/distilroberta-base-ner-wikiann-conll2003-3-class")
nlp = pipeline("ner", model=model, tokenizer=tokenizer, grouped_entities=True)
example = "My name is Philipp and live in Germany"
nlp(example)
```
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4.9086903597787154e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
- mixed_precision_training: Native AMP
### Training results
It achieves the following results on the evaluation set:
- Loss: 0.0520
- Precision: 0.9625
- Recall: 0.9667
- F1: 0.9646
- Accuracy: 0.9914
It achieves the following results on the test set:
- Loss: 0.141
- Precision: 0.917
- Recall: 0.9313
- F1: 0.9241
- Accuracy: 0.9807
### Framework versions
- Transformers 4.6.1
- Pytorch 1.8.1+cu101
- Datasets 1.6.2
- Tokenizers 0.10.3