---
license: cc-by-4.0
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: herbert-base-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: wikiann
      type: wikiann
      config: pl
      split: validation
      args: pl
    metrics:
    - name: Precision
      type: precision
      value: 0.8885878330430295
    - name: Recall
      type: recall
      value: 0.905945803735859
    - name: F1
      type: f1
      value: 0.8971828692395376
    - name: Accuracy
      type: accuracy
      value: 0.9568532096363909
---

<!-- 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. -->

# herbert-base-ner

This model is a fine-tuned version of [allegro/herbert-base-cased](https://huggingface.co/allegro/herbert-base-cased) on the wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2006
- Precision: 0.8886
- Recall: 0.9059
- F1: 0.8972
- Accuracy: 0.9569

## 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: 1e-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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.207         | 1.0   | 2500 | 0.1929          | 0.8566    | 0.8884 | 0.8722 | 0.9499   |
| 0.1528        | 2.0   | 5000 | 0.1979          | 0.8807    | 0.9006 | 0.8905 | 0.9547   |
| 0.1195        | 3.0   | 7500 | 0.2006          | 0.8886    | 0.9059 | 0.8972 | 0.9569   |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3