---
base_model: dbmdz/bert-large-cased-finetuned-conll03-english
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: large-bert-cased-ner-finetuned-wikineural-final
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# large-bert-cased-ner-finetuned-wikineural-final

This model is a fine-tuned version of [dbmdz/bert-large-cased-finetuned-conll03-english](https://huggingface.co/dbmdz/bert-large-cased-finetuned-conll03-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0473
- Precision: 0.9055
- Recall: 0.9156
- F1: 0.9105
- Accuracy: 0.9860

## 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: 9
- eval_batch_size: 8
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.04          | 1.0   | 10321 | 0.0473          | 0.9055    | 0.9156 | 0.9105 | 0.9860   |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1