Model Description

This model is a fine-tuned version of distilroberta-base on ConLL2003 dataset. It achieves the following results on the evaluation set in Named Entity Recognition (NER)/Token Classification task:

Loss: 0.0585
F1: 0.9536

Model Performance

1st Place: This fine-tuned model is topped on the best scores ( F1: 94.6%) from Named Entity Recognition (NER) on CoNLL 2003 (English).
6th Place: This fine-tuned model is ranked in the 6th place from the Token Classification on conll2003 leaderboard

Model Usage

from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline

tokenizer = AutoTokenizer.from_pretrained("jinhybr/distilroberta-ConLL2003")
model = AutoModelForTokenClassification.from_pretrained("jinhybr/distilroberta-ConLL2003")

nlp = pipeline("ner", model=model, tokenizer=tokenizer, grouped_entities=True)
example = "My name is Tao Jin and live in Canada"
ner_results = nlp(example)
print(ner_results)

[{'entity_group': 'PER', 'score': 0.99686015, 'word': ' Tao Jin', 'start': 11, 'end': 18}, {'entity_group': 'LOC', 'score': 0.9996836, 'word': ' Canada', 'start': 31, 'end': 37}]

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 32
eval_batch_size: 16
seed: 24
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 6.0

Training results

Training Loss	Epoch	Step	Validation Loss	F1
0.1666	1.0	439	0.0621	0.9345
0.0499	2.0	878	0.0564	0.9391
0.0273	3.0	1317	0.0553	0.9469
0.0167	4.0	1756	0.0553	0.9492
0.0103	5.0	2195	0.0572	0.9516
0.0068	6.0	2634	0.0585	0.9536

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.17.0
Tokenizers 0.15.1

jinhybr
/

distilroberta-ConLL2003