metadata

license: apache-2.0
base_model: bert-base-cased
tags:
  - generated_from_trainer
datasets:
  - conll2003
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: results
    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: 0.9307273626917367
          - name: Recall
            type: recall
            value: 0.9496802423426456
          - name: F1
            type: f1
            value: 0.9401082882132445
          - name: Accuracy
            type: accuracy
            value: 0.9863866486136458

results

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

Loss: 0.0635
Precision: 0.9307
Recall: 0.9497
F1: 0.9401
Accuracy: 0.9864

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: 5e-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
lr_scheduler_warmup_steps: 500
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.2313	0.2847	500	0.1403	0.8444	0.8696	0.8568	0.9626
0.1088	0.5695	1000	0.0887	0.8717	0.9098	0.8903	0.9765
0.1211	0.8542	1500	0.0846	0.9076	0.9238	0.9156	0.9784
0.0503	1.1390	2000	0.0753	0.9101	0.9354	0.9226	0.9814
0.0493	1.4237	2500	0.0630	0.9170	0.9421	0.9294	0.9833
0.0624	1.7084	3000	0.0705	0.9277	0.9366	0.9321	0.9837
0.0313	1.9932	3500	0.0675	0.9270	0.9426	0.9347	0.9843
0.0335	2.2779	4000	0.0661	0.9284	0.9492	0.9387	0.9857
0.0098	2.5626	4500	0.0693	0.9347	0.9473	0.9410	0.9849
0.0099	2.8474	5000	0.0635	0.9307	0.9497	0.9401	0.9864

Framework versions

Transformers 4.41.2
Pytorch 2.3.1+cu118
Datasets 2.19.2
Tokenizers 0.19.1