---
license: mit
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: hmBERT-CoNLL-cp1
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: conll2003
      type: conll2003
      args: conll2003
    metrics:
    - name: Precision
      type: precision
      value: 0.8690143162744776
    - name: Recall
      type: recall
      value: 0.8887579939414338
    - name: F1
      type: f1
      value: 0.8787752724852317
    - name: Accuracy
      type: accuracy
      value: 0.9810170943499085
---

<!-- 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-cp1

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: 0.0710
- Precision: 0.8690
- Recall: 0.8888
- F1: 0.8788
- Accuracy: 0.9810

## 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: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.06  | 25   | 0.4115          | 0.3593    | 0.3708 | 0.3649 | 0.9002   |
| No log        | 0.11  | 50   | 0.2263          | 0.6360    | 0.6898 | 0.6618 | 0.9456   |
| No log        | 0.17  | 75   | 0.1660          | 0.7250    | 0.7582 | 0.7412 | 0.9564   |
| No log        | 0.23  | 100  | 0.1520          | 0.7432    | 0.7775 | 0.7600 | 0.9597   |
| No log        | 0.28  | 125  | 0.1343          | 0.7683    | 0.8103 | 0.7888 | 0.9645   |
| No log        | 0.34  | 150  | 0.1252          | 0.7973    | 0.8230 | 0.8099 | 0.9691   |
| No log        | 0.4   | 175  | 0.1021          | 0.8118    | 0.8398 | 0.8255 | 0.9724   |
| No log        | 0.46  | 200  | 0.1056          | 0.8153    | 0.8411 | 0.8280 | 0.9727   |
| No log        | 0.51  | 225  | 0.0872          | 0.8331    | 0.8612 | 0.8469 | 0.9755   |
| No log        | 0.57  | 250  | 0.1055          | 0.8226    | 0.8418 | 0.8321 | 0.9725   |
| No log        | 0.63  | 275  | 0.0921          | 0.8605    | 0.8640 | 0.8623 | 0.9767   |
| No log        | 0.68  | 300  | 0.0824          | 0.8600    | 0.8787 | 0.8692 | 0.9788   |
| No log        | 0.74  | 325  | 0.0834          | 0.8530    | 0.8771 | 0.8649 | 0.9787   |
| No log        | 0.8   | 350  | 0.0758          | 0.8646    | 0.8876 | 0.8759 | 0.9800   |
| No log        | 0.85  | 375  | 0.0727          | 0.8705    | 0.8866 | 0.8784 | 0.9810   |
| No log        | 0.91  | 400  | 0.0734          | 0.8717    | 0.8899 | 0.8807 | 0.9811   |
| No log        | 0.97  | 425  | 0.0713          | 0.8683    | 0.8889 | 0.8785 | 0.9810   |


### Framework versions

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