metadata

license: cc-by-sa-4.0
tags:
  - generated_from_trainer
datasets:
  - fin
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: fin4
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: fin
          type: fin
          config: default
          split: train
          args: default
        metrics:
          - name: Precision
            type: precision
            value: 0.9209486166007905
          - name: Recall
            type: recall
            value: 0.9282868525896414
          - name: F1
            type: f1
            value: 0.9246031746031745
          - name: Accuracy
            type: accuracy
            value: 0.9913080347678609

fin4

This model is a fine-tuned version of nlpaueb/sec-bert-num on the fin dataset. It achieves the following results on the evaluation set:

Loss: 0.0549
Precision: 0.9209
Recall: 0.9283
F1: 0.9246
Accuracy: 0.9913

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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	129	0.1041	0.8242	0.8406	0.8323	0.9788
No log	2.0	258	0.0511	0.9173	0.9283	0.9228	0.9902
No log	3.0	387	0.0430	0.9102	0.9283	0.9191	0.9907
0.0598	4.0	516	0.0501	0.9368	0.9442	0.9405	0.9922
0.0598	5.0	645	0.0436	0.9325	0.9363	0.9344	0.9924
0.0598	6.0	774	0.0489	0.9433	0.9283	0.9357	0.9917
0.0598	7.0	903	0.0499	0.932	0.9283	0.9301	0.9919
0.0028	8.0	1032	0.0537	0.9209	0.9283	0.9246	0.9913
0.0028	9.0	1161	0.0540	0.9170	0.9243	0.9206	0.9911
0.0028	10.0	1290	0.0549	0.9209	0.9283	0.9246	0.9913

Framework versions

Transformers 4.25.1
Pytorch 1.13.0+cu116
Datasets 2.7.1
Tokenizers 0.13.2