---
license: mit
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: hmBERT-CoNLL-cp3
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: conll2003
      type: conll2003
      args: conll2003
    metrics:
    - name: Precision
      type: precision
      value: 0.9121408403919614
    - name: Recall
      type: recall
      value: 0.9242679232581622
    - name: F1
      type: f1
      value: 0.9181643400484828
    - name: Accuracy
      type: accuracy
      value: 0.9862154900510105
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# hmBERT-CoNLL-cp3

This model is a fine-tuned version of [dbmdz/bert-base-historic-multilingual-cased](https://huggingface.co/dbmdz/bert-base-historic-multilingual-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0572
- Precision: 0.9121
- Recall: 0.9243
- F1: 0.9182
- Accuracy: 0.9862

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.06  | 25   | 0.4115          | 0.3643    | 0.3728 | 0.3685 | 0.9007   |
| No log        | 0.11  | 50   | 0.2243          | 0.6393    | 0.6908 | 0.6641 | 0.9460   |
| No log        | 0.17  | 75   | 0.1617          | 0.7319    | 0.7637 | 0.7475 | 0.9580   |
| No log        | 0.23  | 100  | 0.1544          | 0.7282    | 0.7637 | 0.7455 | 0.9585   |
| No log        | 0.28  | 125  | 0.1341          | 0.7595    | 0.8117 | 0.7847 | 0.9644   |
| No log        | 0.34  | 150  | 0.1221          | 0.7980    | 0.8251 | 0.8114 | 0.9693   |
| No log        | 0.4   | 175  | 0.1013          | 0.7968    | 0.8344 | 0.8152 | 0.9719   |
| No log        | 0.46  | 200  | 0.1076          | 0.8265    | 0.8403 | 0.8333 | 0.9732   |
| No log        | 0.51  | 225  | 0.0883          | 0.8453    | 0.8635 | 0.8543 | 0.9763   |
| No log        | 0.57  | 250  | 0.0973          | 0.8439    | 0.8633 | 0.8535 | 0.9763   |
| No log        | 0.63  | 275  | 0.0883          | 0.8497    | 0.8655 | 0.8575 | 0.9765   |
| No log        | 0.68  | 300  | 0.0879          | 0.8462    | 0.8642 | 0.8551 | 0.9766   |
| No log        | 0.74  | 325  | 0.0781          | 0.8592    | 0.8834 | 0.8711 | 0.9787   |
| No log        | 0.8   | 350  | 0.0725          | 0.8697    | 0.8928 | 0.8811 | 0.9803   |
| No log        | 0.85  | 375  | 0.0755          | 0.8687    | 0.8943 | 0.8813 | 0.9807   |
| No log        | 0.91  | 400  | 0.0666          | 0.8781    | 0.9004 | 0.8891 | 0.9822   |
| No log        | 0.97  | 425  | 0.0658          | 0.8877    | 0.8995 | 0.8936 | 0.9823   |
| No log        | 1.03  | 450  | 0.0645          | 0.8951    | 0.9036 | 0.8993 | 0.9837   |
| No log        | 1.08  | 475  | 0.0697          | 0.8864    | 0.9039 | 0.8951 | 0.9831   |
| 0.1392        | 1.14  | 500  | 0.0688          | 0.8824    | 0.8994 | 0.8908 | 0.9824   |
| 0.1392        | 1.2   | 525  | 0.0681          | 0.8950    | 0.9049 | 0.8999 | 0.9827   |
| 0.1392        | 1.25  | 550  | 0.0676          | 0.8855    | 0.8977 | 0.8915 | 0.9823   |
| 0.1392        | 1.31  | 575  | 0.0618          | 0.8940    | 0.9088 | 0.9014 | 0.9842   |
| 0.1392        | 1.37  | 600  | 0.0644          | 0.8945    | 0.9076 | 0.9010 | 0.9840   |
| 0.1392        | 1.42  | 625  | 0.0641          | 0.8936    | 0.9086 | 0.9010 | 0.9837   |
| 0.1392        | 1.48  | 650  | 0.0619          | 0.8969    | 0.9120 | 0.9044 | 0.9846   |
| 0.1392        | 1.54  | 675  | 0.0608          | 0.9045    | 0.9105 | 0.9075 | 0.9848   |
| 0.1392        | 1.59  | 700  | 0.0624          | 0.9038    | 0.9143 | 0.9091 | 0.9851   |
| 0.1392        | 1.65  | 725  | 0.0596          | 0.9062    | 0.9170 | 0.9116 | 0.9852   |
| 0.1392        | 1.71  | 750  | 0.0580          | 0.8995    | 0.9143 | 0.9069 | 0.9848   |
| 0.1392        | 1.77  | 775  | 0.0582          | 0.9082    | 0.9172 | 0.9127 | 0.9858   |
| 0.1392        | 1.82  | 800  | 0.0588          | 0.9024    | 0.9179 | 0.9101 | 0.9852   |
| 0.1392        | 1.88  | 825  | 0.0592          | 0.9020    | 0.9219 | 0.9119 | 0.9856   |
| 0.1392        | 1.94  | 850  | 0.0600          | 0.9054    | 0.9182 | 0.9118 | 0.9852   |
| 0.1392        | 1.99  | 875  | 0.0568          | 0.9068    | 0.9202 | 0.9135 | 0.9861   |
| 0.1392        | 2.05  | 900  | 0.0571          | 0.9131    | 0.9212 | 0.9171 | 0.9861   |
| 0.1392        | 2.11  | 925  | 0.0577          | 0.9110    | 0.9204 | 0.9157 | 0.9858   |
| 0.1392        | 2.16  | 950  | 0.0605          | 0.9127    | 0.9243 | 0.9185 | 0.9860   |
| 0.1392        | 2.22  | 975  | 0.0575          | 0.9109    | 0.9224 | 0.9166 | 0.9867   |
| 0.0392        | 2.28  | 1000 | 0.0572          | 0.9121    | 0.9243 | 0.9182 | 0.9862   |
| 0.0392        | 2.33  | 1025 | 0.0567          | 0.9171    | 0.9253 | 0.9212 | 0.9870   |
| 0.0392        | 2.39  | 1050 | 0.0570          | 0.9193    | 0.9295 | 0.9244 | 0.9871   |
| 0.0392        | 2.45  | 1075 | 0.0584          | 0.9155    | 0.9276 | 0.9215 | 0.9867   |
| 0.0392        | 2.51  | 1100 | 0.0591          | 0.9168    | 0.9286 | 0.9227 | 0.9867   |
| 0.0392        | 2.56  | 1125 | 0.0577          | 0.9182    | 0.9312 | 0.9246 | 0.9874   |
| 0.0392        | 2.62  | 1150 | 0.0570          | 0.9184    | 0.9283 | 0.9233 | 0.9870   |
| 0.0392        | 2.68  | 1175 | 0.0563          | 0.9191    | 0.9298 | 0.9245 | 0.9872   |
| 0.0392        | 2.73  | 1200 | 0.0565          | 0.9180    | 0.9313 | 0.9246 | 0.9872   |
| 0.0392        | 2.79  | 1225 | 0.0559          | 0.9190    | 0.9298 | 0.9244 | 0.9873   |
| 0.0392        | 2.85  | 1250 | 0.0562          | 0.9185    | 0.9293 | 0.9239 | 0.9873   |
| 0.0392        | 2.9   | 1275 | 0.0564          | 0.9175    | 0.9285 | 0.9230 | 0.9872   |
| 0.0392        | 2.96  | 1300 | 0.0563          | 0.9181    | 0.9295 | 0.9237 | 0.9873   |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.12.0
- Datasets 2.4.0
- Tokenizers 0.12.1