from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline

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

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: 0.3156
Precision: 0.8332
Recall: 0.8424
F1: 0.8378
Accuracy: 0.9193

It achieves the following results on the test set:

Loss: 0.3023
Precision: 0.8301
Recall: 0.8452
F1: 0.8376
Accuracy: 0.92

Framework versions

Transformers 4.6.1
Pytorch 1.8.1+cu101
Datasets 1.6.2
Tokenizers 0.10.2