metadata

language: en
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - conll2003
metrics:
  - precision
  - recall
  - f1
  - accuracy
widget:
  - text: My name is Scott and I live in Columbus.
  - text: >-
      My name is Scott and I am calling from Buffalo, NY. I would like to file a
      complain with United Airlines.
  - text: Apple was founded in 1976 by Steve Jobs, Steve Wozniak and Ronald Wayne.
base_model: bert-large-uncased
model-index:
  - name: bert-large-uncased-finetuned-ner
    results:
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: conll2003
          type: conll2003
          args: conll2003
        metrics:
          - type: precision
            value: 0.9504719600222099
            name: Precision
          - type: recall
            value: 0.9574896520863632
            name: Recall
          - type: f1
            value: 0.9539679001337494
            name: F1
          - type: accuracy
            value: 0.9885618059637473
            name: Accuracy

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