philschmid/distilroberta-base-ner-conll2003

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distilroberta-base-ner-conll2003

This model is a fine-tuned version of distilroberta-base on the conll2003 dataset.

eval F1-Score: 95,29 (CoNLL-03) test F1-Score: 90,74 (CoNLL-03)

eval F1-Score: 95,29 (CoNLL++ / CoNLL-03 corrected) test F1-Score: 92,23 (CoNLL++ / CoNLL-03 corrected)

Model Usage

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
• —trainbatchsize: 32
• —evalbatchsize: 16
• —seed: 42
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: linear
• —num_epochs: 6.0
• —mixedprecisiontraining: 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