metadata

license: mit
tags:
  - generated_from_trainer
datasets:
  - conll2003
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: deberta-finetuned-ner
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: conll2003
          type: conll2003
          args: conll2003
        metrics:
          - name: Precision
            type: precision
            value: 0.9577488309953239
          - name: Recall
            type: recall
            value: 0.9651632446987546
          - name: F1
            type: f1
            value: 0.961441743503772
          - name: Accuracy
            type: accuracy
            value: 0.9907182964622135
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: conll2003
          type: conll2003
          config: conll2003
          split: test
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9108823919384779
            verified: true
          - name: Precision
            type: precision
            value: 0.9308372971460548
            verified: true
          - name: Recall
            type: recall
            value: 0.9213792387183881
            verified: true
          - name: F1
            type: f1
            value: 0.9260841198729938
            verified: true
          - name: loss
            type: loss
            value: 0.8661637306213379
            verified: true

deberta-finetuned-ner

This model is a fine-tuned version of microsoft/deberta-base on the conll2003 dataset. It achieves the following results on the evaluation set:

Loss: 0.0515
Precision: 0.9577
Recall: 0.9652
F1: 0.9614
Accuracy: 0.9907

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

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: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0742	1.0	1756	0.0526	0.9390	0.9510	0.9450	0.9868
0.0374	2.0	3512	0.0528	0.9421	0.9554	0.9487	0.9879
0.0205	3.0	5268	0.0505	0.9505	0.9636	0.9570	0.9900
0.0089	4.0	7024	0.0528	0.9531	0.9636	0.9583	0.9898
0.0076	5.0	8780	0.0515	0.9577	0.9652	0.9614	0.9907

Framework versions

Transformers 4.20.1
Pytorch 1.12.0+cu113
Datasets 2.3.2
Tokenizers 0.12.1