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---
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
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