bert-large-uncased-finetuned-ner

This model is a fine-tuned version of bert-large-uncased on the conll2003 dataset. It achieves the following results on the evaluation set:

• Loss: 0.0778
• Precision: 0.9505
• Recall: 0.9575
• F1: 0.9540
• Accuracy: 0.9886

Model description

More information needed

Limitations and bias

This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Furthermore, the model occassionally tags subword tokens as entities and post-processing of results may be necessary to handle those cases.

How to use

You can use this model with Transformers pipeline for NER.

from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("Jorgeutd/bert-large-uncased-finetuned-ner")
model = AutoModelForTokenClassification.from_pretrained("Jorgeutd/bert-large-uncased-finetuned-ner")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "My name is Scott and I live in Ohio"
ner_results = nlp(example)
print(ner_results)

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 16
• eval_batch_size: 64
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.1997	1.0	878	0.0576	0.9316	0.9257	0.9286	0.9837
0.04	2.0	1756	0.0490	0.9400	0.9513	0.9456	0.9870
0.0199	3.0	2634	0.0557	0.9436	0.9540	0.9488	0.9879
0.0112	4.0	3512	0.0602	0.9443	0.9569	0.9506	0.9881
0.0068	5.0	4390	0.0631	0.9451	0.9589	0.9520	0.9882
0.0044	6.0	5268	0.0638	0.9510	0.9567	0.9538	0.9885
0.003	7.0	6146	0.0722	0.9495	0.9560	0.9527	0.9885
0.0016	8.0	7024	0.0762	0.9491	0.9595	0.9543	0.9887
0.0018	9.0	7902	0.0769	0.9496	0.9542	0.9519	0.9883
0.0009	10.0	8780	0.0778	0.9505	0.9575	0.9540	0.9886

Framework versions

• Transformers 4.16.2
• Pytorch 1.8.1+cu111
• Datasets 1.18.3
• Tokenizers 0.11.0