from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline

tokenizer = AutoTokenizer.from_pretrained("philschmid/distilroberta-base-ner-conll2003")
model = AutoModelForTokenClassification.from_pretrained("philschmid/distilroberta-base-ner-conll2003")

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: 4.9902376275441704e-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: 6.0
mixed_precision_training: Native AMP

Training results

CoNNL2003

It achieves the following results on the evaluation set:

Loss: 0.0583
Precision: 0.9493
Recall: 0.9566
F1: 0.9529
Accuracy: 0.9883

It achieves the following results on the test set:

Loss: 0.2025
Precision: 0.8999
Recall: 0.915
F1: 0.9074
Accuracy: 0.9741

CoNNL++ / CoNLL2003 corrected

It achieves the following results on the evaluation set:

Loss: 0.0567
Precision: 0.9493
Recall: 0.9566
F1: 0.9529
Accuracy: 0.9883

It achieves the following results on the test set:

Loss: 0.1359
Precision: 0.92
Recall: 0.9245
F1: 0.9223
Accuracy: 0.9785

Framework versions

Transformers 4.6.1
Pytorch 1.8.1+cu101
Datasets 1.6.2
Tokenizers 0.10.2