metadata

license: apache-2.0
base_model: om-ashish-soni/pos-ner-tagging-v2
tags:
  - generated_from_trainer
datasets:
  - conll2003
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: pos-ner-tagging-v2
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: conll2003
          type: conll2003
          config: conll2003
          split: validation
          args: conll2003
        metrics:
          - name: Precision
            type: precision
            value: 0.9393653920267203
          - name: Recall
            type: recall
            value: 0.9408358887483113
          - name: F1
            type: f1
            value: 0.9401000653531749
          - name: Accuracy
            type: accuracy
            value: 0.9270324365691411

pos-ner-tagging-v2

This model is a fine-tuned version of om-ashish-soni/pos-ner-tagging-v2 on the conll2003 dataset. It achieves the following results on the evaluation set:

Loss: 0.6442
Precision: 0.9394
Recall: 0.9408
F1: 0.9401
Accuracy: 0.9270

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: 16

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.3297	1.0	1756	0.4190	0.9189	0.9231	0.9210	0.9051
0.2521	2.0	3512	0.3836	0.9210	0.9300	0.9255	0.9114
0.1932	3.0	5268	0.4155	0.9295	0.9338	0.9316	0.9183
0.1325	4.0	7024	0.3969	0.9328	0.9356	0.9342	0.9211
0.0973	5.0	8780	0.4247	0.9332	0.9367	0.9349	0.9222
0.0799	6.0	10536	0.4606	0.9338	0.9374	0.9356	0.9229
0.0554	7.0	12292	0.4836	0.9333	0.9379	0.9356	0.9239
0.0415	8.0	14048	0.5271	0.9361	0.9391	0.9376	0.9245
0.0285	9.0	15804	0.5363	0.9366	0.9397	0.9381	0.9253
0.022	10.0	17560	0.5653	0.9377	0.9396	0.9387	0.9258
0.0146	11.0	19316	0.5962	0.9374	0.9400	0.9387	0.9259
0.0121	12.0	21072	0.6061	0.9385	0.9401	0.9393	0.9266
0.0085	13.0	22828	0.6263	0.9384	0.9403	0.9394	0.9261
0.0062	14.0	24584	0.6365	0.9381	0.9399	0.9390	0.9259
0.0053	15.0	26340	0.6386	0.9384	0.9402	0.9393	0.9264
0.0042	16.0	28096	0.6442	0.9394	0.9408	0.9401	0.9270

Framework versions

Transformers 4.33.2
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.13.3