metadata

license: mit
tags:
  - generated_from_trainer
datasets:
  - tner/ontonotes5
metrics:
  - precision
  - recall
  - f1
  - accuracy
widget:
  - text: 'I am Jack. I live in California and I work at Apple '
    example_title: Example 1
  - text: 'Wow this book is amazing and costs only 4€ '
    example_title: Example 2
base_model: distilbert-base-cased
model-index:
  - name: distilbert-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: 0.8535359959297889
            name: Precision
          - type: recall
            value: 0.8788553467356427
            name: Recall
          - type: f1
            value: 0.8660106468785288
            name: F1
          - type: accuracy
            value: 0.9749625470373822
            name: Accuracy

distilbert-finetuned-ner-ontonotes

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

Loss: 0.1448
Precision: 0.8535
Recall: 0.8789
F1: 0.8660
Accuracy: 0.9750

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 domain.

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
0.0937	1.0	7491	0.0998	0.8367	0.8587	0.8475	0.9731
0.0572	2.0	14982	0.1084	0.8338	0.8759	0.8543	0.9737
0.0403	3.0	22473	0.1145	0.8521	0.8707	0.8613	0.9748
0.0265	4.0	29964	0.1222	0.8535	0.8815	0.8672	0.9752
0.0148	5.0	37455	0.1365	0.8536	0.8770	0.8651	0.9747
0.0111	6.0	44946	0.1448	0.8535	0.8789	0.8660	0.9750

Framework versions

Transformers 4.22.1
Pytorch 1.12.1+cu113
Datasets 2.5.1
Tokenizers 0.12.1