metadata

language:
  - en
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - tner/ontonotes5
metrics:
  - precision
  - recall
  - f1
  - accuracy
widget:
  - text: 'Hi! I am jack. I live in California and I work for Apple '
    example_title: Example 1
  - text: 'Thi book is amazing! I bought it on Amazon for 4$. '
    example_title: Example 2
base_model: bert-base-cased
model-index:
  - name: bert-finetuned-ner-ontonotes
    results:
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: ontonotes5
          type: ontonotes5
          config: ontonotes5
          split: train
          args: ontonotes5
        metrics:
          - type: precision
            value: 0.8567258883248731
            name: Precision
          - type: recall
            value: 0.8841595180407308
            name: Recall
          - type: f1
            value: 0.8702265476459025
            name: F1
          - type: accuracy
            value: 0.9754933764288157
            name: Accuracy

bert-finetuned-ner-ontonotes

This model is a fine-tuned version of bert-base-cased on the ontonotes5 dataset. It achieves the following results on the evaluation set:

Loss: 0.1503
Precision: 0.8567
Recall: 0.8842
F1: 0.8702
Accuracy: 0.9755

Model description

Token classification experiment, NER, on business topics.

Intended uses & limitations

The model can be used on token classification, in particular NER. It is fine tuned on business topic.

Training and evaluation data

The dataset used is ontonotes5

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 6

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0842	1.0	7491	0.0950	0.8524	0.8715	0.8618	0.9745
0.0523	2.0	14982	0.1044	0.8449	0.8827	0.8634	0.9744
0.036	3.0	22473	0.1118	0.8529	0.8843	0.8683	0.9760
0.0231	4.0	29964	0.1240	0.8589	0.8805	0.8696	0.9752
0.0118	5.0	37455	0.1416	0.8570	0.8804	0.8685	0.9753
0.0077	6.0	44946	0.1503	0.8567	0.8842	0.8702	0.9755

Framework versions

Transformers 4.22.1
Pytorch 1.12.1+cu113
Datasets 2.5.1
Tokenizers 0.12.1