---
language:
- en
license: apache-2.0
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: albert-base-v2
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: conll2003
      type: conll2003
      args: default
    metrics:
    - name: precision
      type: precision
      value: 0.9252213840603477
    - name: recall
      type: recall
      value: 0.9329732113328189
    - name: f1
      type: f1
      value: 0.9290811285541773
    - name: accuracy
      type: accuracy
      value: 0.9848205157332728
---

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# albert-base-v2

This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- precision: 0.9252
- recall: 0.9330
- f1: 0.9291
- accuracy: 0.9848

## 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:
- num_train_epochs: 5
- train_batch_size: 16
- learning_rate: 2e-05
- weight_decay_rate: 0.01
- num_warmup_steps: 0
- fp16: True

### Framework versions

- Transformers 4.16.2
- Pytorch 1.8.1+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0